Received: 13 May 2018 Revised: 30 July 2018 Accepted: 14 October 2018 DOI: 10.1002/joc.5894

RESEARCH ARTICLE

Observed changes in temperature extremes over China–Pakistan Economic Corridor during 1980–2016

Safi Ullah1 | Qinglong You1,2 | Waheed Ullah3 | Amjad Ali4 | Wenxin Xie1 | Xinru Xie1

1Key Laboratory of Meteorological Disaster, Ministry of Education (KLME); Joint International This study uses the daily maximum and minimum temperature data sets of 48 mete- Research Laboratory of Climate and orological stations to assess the observed changes in temperature extremes over Environmental Change (ILCEC), Collaborative China–Pakistan Economic Corridor (CPEC) during 1980–2016. The nonparametric Innovation Center on Forecast and Evaluation of – ’ Meteorological Disasters (CIC-FEMD); Earth Mann Kendall, Sen s slope estimator, least squares method, and two-tailed simple System Modeling Center, Nanjing University of t-test methods were used to assess the trend in nine temperature extreme indices. Information Science and Technology (NUIST), The results of the study indicated that the trends in annual mean anomalies of cold Nanjing, China nights, cold days, and frost days significantly decreased at the rates of −1.09, 2 Department of Atmospheric and Oceanic − − Sciences and Institute of Atmospheric Sciences, 1.58, and 1.05 day/decade, respectively. The number of warm nights, warm Fudan University, Shanghai, China days, and summer days exhibited significant positive trends at the rates of 1.49, 3Collaborative Innovation Center on Forecast and 1.04, and 4.32 day/decade, respectively. In addition, the trends of the warmest day Evaluation of Meteorological Disasters (CIC- and coldest day also increased significantly at the rates of 0.45 and 0.51 C/decade, FEMD), School of Geographical Sciences, respectively. The trend of diurnal temperature range decreased significantly with Nanjing University of Information Science and − Technology (NUIST), Nanjing, China 0.35 C/decade. The spatial distribution of temperature extreme indices indicated 4Centre for Disaster Preparedness and that the number of cold nights, cold days, and diurnal temperature range have been Management, University of Peshawar, Peshawar, decreased, while the trends of warm nights, warm days, summer days, warmest Pakistan day, and coldest day have been increased over the whole country. Large-scale Correspondence atmospheric circulation changes derived from ERA-Interim reanalysis show that a Qinglong You, Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric weak (strong) wind pressure, increasing (decreasing) geopotential height, and rapid Sciences, Fudan University, Shanghai 200438, warming (cooling) over the northern (southern) region have contributed to the China. changes in temperature extremes in Pakistan to some extent. With respect to Email: [email protected] elevation-dependent warming (EDW), the trends of cold nights, warm days, sum- Funding information Jiangsu Natural Science Funds for Distinguished mer days, warmest day, and diurnal temperature range showed significant positive Young Scholar, Grant/Award Number: correlations with increasing elevation. However, the trends of cold days, warm BK20140047; National Key R&D Program of nights, frost days, and coldest day showed significant negative relationships with China , Grant/Award Number: 2017YFA0603804; increasing elevation. The findings of this study will be helpful in the adaptation of National Natural Science Foundation of China, Grant/Award Number: 41771069; The Priority climate change and mitigation of climatological disasters in the region. The study Academic Program Development of Jiangsu recommends that future studies should investigate the natural and anthropogenic Higher Education Institutions” (PAPD) drivers of temperature extremes and the possible mechanism of EDW in the CPEC region.

KEYWORDS CPEC, elevation-dependent warming, Mann–Kendall test, Sen’s slope estimator, temperature extremes

1 | INTRODUCTION environmental impacts on human life. Recent studies indi- cated that climate change is one of the main drivers of rising Temperature extremes are among the most destructive natu- temperature and extreme temperature events in the present ral calamities and have significant socio-economic and eco- world (Mahmood and Babel, 2014; Herring et al., 2015;

Int J Climatol. 2018;1–19. wileyonlinelibrary.com/journal/joc © 2018 Royal Meteorological Society 1 2 ULLAH ET AL.

Mahmood et al., 2015; Trenberth et al., 2015). According to large variations in regional scale due to disproportional the Intergovernmental Panel on Climate Change (IPCC) impacts of climate change over diverse geographical regions Fifth Assessment Report (AR5), the global average tempera- (Ren et al., 2011; Li et al., 2012c). Recent studies indicated ture has been increased by 0.85 C during 1800–2012 and is that the frequency of temperature extremes has been expected to rise during the 21st century (IPCC, 2013). Due increased over the Tibetan Plateau, South Asian, and the to this increase, the frequency, magnitude, extent, and dura- Hindu Kush Himalayan (HKH) region including Pakistan tion of extreme temperature events (heat waves, severe win- (Yan and Liu, 2014; Sheikh et al., 2015; Ren et al., 2017; ter, hot [cold] days and nights) have also been increased Sun et al., 2017; You et al., 2017). Over the last century, the over the last few decades (Mastrandrea et al., 2011; Donat warming trend was also evident over the Arab region with et al., 2014; Lelieveld et al., 2016; Rahimi and Hejabi, increased numbers of warm days (TX90), warm nights 2017). In recent years, several studies have reported different (TN90), warmest day (TXx) and decreased frequencies of cold days (TX10) and cold nights (TN10) (Almazroui et al., temperature extremes across the globe; for example, the 2014; Donat et al., 2014; Islam et al., 2014). In Loess Pla- 2015 heat waves over Pakistan, India and 2003 heat waves teau of China, the occurrence of warm extremes, including over Europe and Russian (Cheng et al., 2012; Frías et al., summer days (SU), warm days (TX90), and warm nights 2012; Lau and Kim, 2012; Herring et al., 2015; Rahmstorf (TN90) increased while cold extremes such as frost days et al., 2016; Panda et al., 2017). These temperature extremes (FD), cold days (TX10) and cold nights (TN10) decreased have adversely affected human well-being and resulted in during the period of 1960–2013 (Sun et al., 2015; 2016; extensive loss of agricultural production, biodiversity, Yan et al., 2015). Similarly, the frequency, extent, and inten- hydropower generation, and food insecurity (Sen et al., sity of hot days are increasing with substantial rising in 2007; Ashiq et al., 2010; Del Río et al., 2013; Abid and monthly maximum temperature over different river basins of Ashfaq, 2016; Iqbal et al., 2016; Sen et al., 2016; Berardy India (Deshpande et al., 2016). In Bangladesh, the frequen- and Chester, 2017). cies of hot days (>32 C) and hot nights (>25 C) have sig- Under the background of recent global warming, more nificantly increased during 1958–2012 (Shahid et al., 2016). severe temperature extreme events have been predicted with In Pakistan, several studies have been carried out to dramatic impacts on human life (IPCC, 2013; Mahmood and assess the observed changes in extreme temperature events Babel, 2014; Rahimi and Hejabi, 2017). The accurate projec- (Abbas, 2013;Del Río et al., 2013; Iqbal et al., 2016). The tion of future extremes is mainly based on the assessment of maximum and minimum temperature extremes have gener- long-term variations and trends in past temperature extreme ally increased at all timescales during 1952–2009 over events (Donat et al., 2014; Sheikh et al., 2015; Grotjahn Pakistan (Del Río et al., 2013; Iqbal et al., 2016). The num- et al., 2016). Therefore, understanding and analysing the ber of extreme maximum (minimum) temperature events long-term variability and tendency of historical temperature showed a positive (negative) trend over Pakistan during extremes is critical to detect and quantify their adverse 1965–2009 (Zahid and Rasul, 2011). The Indus Delta and impacts on human society (Ren and Zhou, 2014; Ren et al., central parts of Pakistan experienced an increased warming 2017; Sun et al., 2017). The assessment of extreme tempera- trend with increasing frequencies of SU, warm days and ture events over different climatic and geographic regions is warm nights (Abbas, 2013). The southern parts of Pakistan essential for understating uncertainties in responses to recent also experienced a warming trend during the last century global warming trends (Sun et al., 2015; 2016; Yan et al., (Ren et al., 2017). However, the above-cited studies are 2015). The analysis can help to design/develop climate either focused on a small region, limited number of stations, models and climate databases that can predict the origin, short time periods, different indices, data sources or method- magnitude, duration, and direction of future temperature ologies (Ullah et al., 2018). Moreover, no attempt has been extremes (Ren et al., 2011; Iqbal et al., 2016). Moreover, made so far to conduct a detailed study on the analysis of this information can play a key role in long-term planning extreme temperature events over the China–Pakistan Eco- for adaptation and mitigation of climate change at national nomic Corridor (CPEC). Thus, the present study is an and regional levels (Frías et al., 2012; Gu et al., 2012). advancement regarding the extended target region, proposed Recently, many studies have reported a high spatiotem- timescale, stations’ numbers, and elevation-dependent warm- poral variability in the patterns of temperature extremes ing (EDW) phenomenon. across the globe (Galarneau et al., 2012; Abbas, 2013; van The CPEC is a pilot project of the One Belt and One Wijngaarden, 2015; Yao and Chen, 2015; Lelieveld et al., Road Initiative of China (Irshad et al., 2015). The CPEC 2016). Since 1950, both the global maximum temperature project is initiated with the aims to ensure peace, integration, and minimum temperature showed a warming trend over dif- cooperation, and economic growth in the region (Javaid, ferent regions (Donat et al., 2013). Sillmann et al. (2013) 2016). However, climate extremes could become hurdles in reported that the annual occurrences of warm nights (cold achieving the desired targets. Related studies indicated that nights) have significantly increased (decreased) over 70% of the CPEC region is hypersensitive to global warming the world. Similarly, extreme temperature events also show induced climate (Yamada et al., 2016; Asmat and Athar, ULLAH ET AL. 3

2017; Iqbal and Athar, 2018). Due to climate change, the 3 | MATERIALS AND METHODS region experienced frequent temperature extremes such as heat waves, cold spells, and meteorological drought (Zahid 3.1 | Data and Rasul, 2012). These temperature extremes resulted in The daily maximum and minimum temperature data of significant consequences for the local population (Abbas, 48 meteorological stations for the period of 1980–2016 were 2013; Mueller et al., 2014). It is, therefore, critical to assess the spatiotemporal changes in temperature extremes on local obtained from Pakistan Meteorological Department. The and regional scales and to propose effective recommenda- detail information of the selected stations is shown in tions for climate change adaptation and mitigation in the Figure 1 and Table 1. The stations were selected on the basis region. The present study has thus been framed to document of their vicinity to the CPEC routes, temporal coverage, the observed changes in temperature extremes over the completeness, and homogeneity of the data. The study has CPEC for the period of 1980–2016. The study also aimed to used nine temperature extremes indices selected from the list investigate EDW and its impact on temperature extremes in of 27 extreme climate event indices recommended by the the region. Expert Team for Climate Change Detection Monitoring and Indices ETCCDMI (Zhang et al., 2011; Donat et al., 2014; Sun et al., 2016; 2017). The selected indices include both 2 | STUDY AREA percentile-based (4) and absolute value based (5) indices. These indices are significant for the climatology of the target The CPEC is a flag project of the One Belt and One Road region; therefore, these were selected after a thorough Initiative and is stretched from south to northeast of Pakistan review. The detail of the selected indices is given in Table 2. (Irshad et al., 2015). The geographical coordinates of CPEC To investigate changes in large-scale atmospheric, circula- 0 0 0 0 are 25 24 –36 10 north latitude and 63 21 –74 59 east lon- tion monthly mean geopotential height, wind fields and air gitude (Ullah et al., 2018). The corridor links the Gawadar temperature at 850 hPa derived from the ERA-Interim reana- Port of Pakistan to the Kashgar city of China (Javaid, 2016; lysis data/European Centre for Medium-Range Weather Markey, 2016). The total estimated length of the corridor is Forecasts (ECMWF) during 1980–2016 were also analysed. 4,918 km with two major routes, i.e., eastern and western The data set is available at http://www.ecmwf.int (Dee routes (Ullah et al., 2019). The western route lies in the west et al., 2011). of Pakistan, covering most parts of the Khyber Pakhtunkhwa and Baluchistan Provinces, while the eastern route is located 3.2 | Methods in the east of Pakistan, passes through major cities of the Punjab and Sindh provinces (Ullah et al., 2018). The CPEC 3.2.1 | Data quality control is extended from north to south of Pakistan and its networks The quality of the data ensured before applying the Mann– of developmental projects and economic zones will cover Kendall (MK) and Sen’s slope (SS) estimator tests. For this entire Pakistan. Therefore, the present study considered the purpose, the runs test was used to check the homogeneity of whole country as a study area. the temperature time series (Thom, 1966). The homogeniza- Pakistan is situated in the northwest of South Asia with tion technique aimed to detect artificial changes resulted geographical coordinates of 61–78E longitude and 23.6– from unavoidable errors in the observation, instrumentations, 38N latitude. The country has a diverse and multifarious relocations, replacements, environment, and procedures dur- topography which has a profound influence on its climate ing the data collection process (Tao et al., 2014; Yan et al., from region to region (Asmat et al., 2017; Iqbal and Athar, 2015; Sun et al., 2016). The test is recommended by the 2018). The coldest annual temperature (<0 C) can be World Meteorological Organization (WMO) to check the observed in the extreme north (the Himalayan region) and homogeneity of meteorological data sets. This test has been the highest average temperature (>35 C) can be recorded in widely used by different researchers in hydrometeorological the central and southeastern parts of the country (Asmat and studies (Munoz-Diaz and Rodrigo, 2004; Modarres and De Athar, 2017; Iqbal and Athar, 2018). Based on average tem- Paulo, 2007; Nasri and Modarres, 2009; Dikbas et al., 2010; peratures, November–February are considered as the coldest Del Río et al., 2013; Iqbal et al., 2016). All the time series months and May–September as the warmer months of the was founded homogenous at 0.05 significance level. year. The average temperatures are below zero in the coldest months at high elevation in the north, and above 20 Cin 3.2.2 | Statistical analysis the central and southern parts (Asmat et al., 2017). In the The nonparametric MK test was applied to determine the warmest months, the average temperatures do not exceed significance of the monotonic trend in temperature extreme 15 C in the north and 35 C in the south (Asmat and Athar, indices (Mann, 1945; Kendall, 1955; Sonali and Kumar, 2017). The normal annual minimum temperature ranges 2013). Similarly, the nonparametric SS estimator test (Sen, from 0 to 20 C and the normal annual maximum tempera- 1968) was employed to quantify the slope of the trend in ture ranges from 15 to 35 C (Gadiwala et al., 2013). temperature extreme indices (Lu et al., 2016; Thomas and 4 ULLAH ET AL.

Kashghar

Gup Gil Chi Bun Chl Drh Ast 35°N Dir Sad Blk Skr Pch Psh Cht Mzf Kak Mur Isb Kht Ktl Mwl Jhl Slk DIK Sgd Zhb Fsl Lhr Qta Mul Bwr 30°N Sib Brk Dal Bwl Nok Jcb Khp Legend Rhr Met Stations Pan Kdr CEPC Routes Sba Pakistan

Hyd Chr Elevation (m) Jiw Psn Kch High : > 6000 25°N Gawadar Bdn

km Low : 0 0 400 800 1200 1600

60°E 65°E 70°E 75°E 80°E

FIGURE 1 Topographic map of the study area with CPEC routes and selected meteorological stations [Colour figure can be viewed at wileyonlinelibrary.com]

Prasannakumar, 2016; Salman et al., 2017). The correlation monthly means of maximum, minimum, and mean tempera- between trends of temperature extreme indices and elevation ture of the study area shows that January is the coldest was determined by the least squares method (Grinstead and month while July is the warmest month of the year. The low- Snell, 1997; Casella and Berger, 2002; Moore and McCabe, est values for maximum (16 C), minimum (4 C), and mean 2003). In addition, the two-tailed simple t-test method was temperature (10 C) are recorded in the month of January. applied to detect the significance of the linear trends (Sun Similarly, the highest degree of maximum (37 C), mini- et al., 2017). mum (22 C), and mean temperatures (30 C) are obtained in July. The results concur the findings of previous studies (Gadiwala et al., 2013; Asmat et al., 2017; Asmat and Athar, 4 | RESULTS AND DISCUSSION 2017).

4.1 | Climatology of the region 4.2 | Trends in annual mean anomalies of temperature The climatological annual and monthly mean temperatures extreme indices of the study area are given in Figure 2. The distribution of The annual mean anomaly time series of temperature temperature shows high variability both with latitudinal and extreme indices (1980–2016) and their linear trends are longitudinal variations. The northern and southwestern parts shown in Figure 3 and Table 3. The analysis indicates that comprised high elevated mountainous with a humid climate. the warming trend is obvious over the target region as the The mean annual temperature of these areas ranges from cold (warm) indices showed linearly decreasing (increasing) 0to20 C. Similarly, the central regions of the country com- and significant trends. The trends of cold nights (TN10p), prised Indus plains with a continental climate and mean cold days (TX10), FD, and diurnal temperature range (DTR) annual temperature of 21–25 C. The eastern part of the exhibited significant negative trends during the study period. country comprised of a tropical climate, while the southern The trends in the frequency of cold nights (TN10p), cold part is influenced by coastal climate. The mean annual tem- days (TX10p), and FD have been decreased with −1.09, perature of these areas ranges from 26 to 30 C. However; −1.58, and −1.05 day/decade, respectively. The results are the southwestern hilly areas exhibit mean annual temperature in agreement with the findings of previous studies (Sun in the ranges of 21–25 C. Moreover, the multi-year et al., 2017). They reported a significant decrease in the ULLAH ET AL. 5

TABLE 1 Information of selected meteorological stations across CPEC TABLE 2 List of selected temperature extreme indices based on ETCCDMI Code Station Latitude (0) Longitude (0) Elevation (m) Ast Astore 35.35 74.86 2,168 Indicator Category name ID Definition Bdn Badin 24.38 68.54 9 Percentile based Cold nights TN10p Days when daily minimum Bwr Bahawalnagar 29.82 73.28 161 indices temperature <10th Bwl Bahawalpur 29.4 71.7 110 percentile Blk Balakot 34.55 73.21 995 Cold days TX10p Days when daily maximum temperature Brk Barkhan 29.88 69.72 1,097 <10th percentile Bun Bunji 35.67 74.63 1,372 Warm nights TN90p Days when daily minimum Cht Cherat 33.92 71.9 1,372 temperature >90th percentile Chr Chhor 25.31 69.47 5 Warm days TX90p Days when daily Chl Chilas 35.42 74.1 1,250 maximum temperature Chi Chitral 35.83 71.78 1,498 >90th percentile Dal Dalbandin 28.53 64.24 848 Absolute value based FD FD Annual count when daily DIK Dera Ismail Khan 31.49 70.56 171 indices minimum temperature <0 C Dir Dir 35.2 71.85 1,375 SU SU25 Annual count when daily Drh Drosh 35.55 71.85 1,465 maximum temperature Fsl Faisalabad 31.26 73.1 185 >25 C Gil Gilgit 35.92 74.33 1,460 Warmest day TXx Monthly maximum value of daily maximum Gup Gupis 36.17 73.4 2,156 temperature Hyd Hyderabad 25.23 68.25 28 Coldest day TNn Monthly minimum value Isb Islamabad 33.8 73.08 508 of daily minimum temperature Jcb Jacobabad 28.18 68.28 55 DTR DTR Monthly mean difference Jhl Jhelum 32.56 73.44 287 between daily maximum Jiw Jiwani 25.04 61.8 56 and minimum temperature Kak Kakul 34.18 73.15 1,308 Kch Karachi 24.89 67.16 22 Khp Khanpur 28.39 70.41 88 also found a significant negative trend in cold nights Khr Khuzdar 27.5 66.38 1,231 (TN10p), cold days (TX10p), and FD at the rates of −2.38, Kht Kohat 33.45 71.53 564 −0.85, and −4.32 day/decade, respectively, in the target Ktl Kotli 33.31 73.54 614 region. Moreover, the trends of warm nights (TN90p), warm Lhr Lahore 31.35 74.24 214 days (TX90p), and summer days (SU25) indices were Mwl Mianwali 32.58 71.55 205 increased at the rates of 1.49, 1.04, and 4.32 day/decade, Mul Multan 30.12 71.26 122 respectively. The results are consistent with the findings of Mur Murree 33.9 73.4 2,134 previous studies. Klein et al. (2006) detected an increasing Mzf Muzaffarabad 34.37 73.48 838 trend in warm nights (TN90p) and warm days (TX90p) with Nok Nokkundi 28.49 62.45 682 4.31 and 2.55 day/decade, respectively, in the study region. Pan Panjgur 26.58 64.1 968 Sheikh et al. (2015) also reported a positive trend in the Par Parachinar 33.9 70.1 1,775 number of warm days (TX90p) and summer day (SU25) at Psn Pasni 25.22 63.28 9 the rates of 1.7 and 4.00 day/decade, respectively, in the Psh Peshawar 34.02 71.58 328 South Asian region. Qta Quetta 30.11 67.57 1,719 Furthermore, the extreme temperatures of the warmest Rhr Rohri 27.69 68.85 66 day (TXx) and coldest day (TNn) were also increased signif- Sad Saidu Sharif 34.73 72.35 961 icantly at the rates of 0.45 and 0.51 C/decade, respectively. Sgd Sargodha 32.03 72.4 187 The results are in agreement with the findings of past studies Sba Shaheed Benazirabad 26.15 68.22 37 (Klein et al., 2006; Sun et al., 2017). They reported a signifi- Slk Sialkot 32.31 74.32 255 cant increase in the number of the warmest day (TXx) and Sib Sibbi 29.33 67.55 133 coldest (TNn) at the rates of 0.28, 0.42, and 0.17, 0.73 C/ Skr Skardu 35.18 75.68 2,317 decade, respectively, in the study area. The DTR exhibited a Zhb Zhob 31.21 69.28 1,405 significant negative trend (−0.35 C/decade) during 1980–2016. Similar results were reported by You et al. number of cold nights (TN10p), cold days (TX10p), and FD (2017)) and Khan et al. (2018). They found a decreasing with −0.98, −0.51, and −3.63 day/decade, respectively, in trend of −0.15 and −0.20 C/decade, respectively, in the HKH region including Pakistan. Similarly, You et al. (2017) study area. Though, the trends’ magnitudes noted here vary 6 ULLAH ET AL.

(a) (b) 40

35°N 30

20 30°N T Annual mean 0 – 10

11 – 15 Temperature (°C) T 10 max 16 – 20 T mean 25°N 21 – 25 T min 26 – 30 0 65°E 70°E 75°E JFMAMJ J A SOND Months

FIGURE 2 Climatological annul and monthly means of Tmax, Tmin, and Tmean of the study area during 1980–2016; (a) annual Tmean, (b) monthly means of

Tmax, Tmin, and Tmean. The vertical bars represent the standard deviations of the Tmax, Tmin, and Tmean [Colour figure can be viewed at wileyonlinelibrary.com] from that of previous studies, which could be the results of trend of −0.61 day/decade in central Pakistan (Punjab). The either different study period or statistical methods or/and sta- study periods and stations’ density of the above studies are tions’ density (Khan et al., 2018). different from the present study, which may also affect the outcomes of the study. However, Klein et al. (2006) and 4.3 | Cold nights and cold days Khan et al. (2018) reported an increasing trend of cold days (4–8 day/decade) in northern Pakistan, which contradict the Figure 4 shows the spatial analysis of the trend in cold nights findings of the present study. Though, they have used differ- (TN10p) and cold days (TX10p) in the study area. The ent data type (gridded data), different statistical tool (modi- results indicate that the trends of cold nights and cold days fied Mann–Kendall test) and different temporal coverage, were apparently decreased over the whole country. In cold which may have a differential impact on the results. Gener- nights (TN10p), central and southeastern parts of the country ally, the trend of cold days (TX10p) was statistically signifi- experienced obvious warming with the highest negative cant at 30 stations out of which 29 stations were negatively trend (−2.96 day/decade) at Barkhan and Lahore stations. Similarly, the northern part of the country exhibited the low- significant and one station was positively significant. Irre- est negative trend in cold nights (TN10p). The results are spective of the whole country, some of the stations located consistent with the findings of Sun et al. (2017)). However, in the coastal belt and mountainous regions exhibited a slight their trend value (−0.97 day/decade) is slightly low from the increasing but an insignificant trend in cold nights (TN10p) present results which could be the result of the different and cold days (TX10p). The results are consistent with the study period. In contrast, Klein et al. (2006) found that the findings of a recent study conducted by (Khan et al., 2018). − cold nights (TN10p) are decreasing at the rates of 4–8 day/ They reported that a decreasing trend ( 1.00 day/decade) in decade, in northern Pakistan, but their study period was lim- cold nights (TN10p) in northern Pakistan. On the other hand, ited to 2000, which might be the reason of variations in trend the results contradict the findings of previous studies who magnitude. Overall, the trend of cold nights (TN10p) was reported a decreasing trend in cold nights (TN10p) and cold significant at 27 stations, out of which were negatively sig- days (TX10p) in this region (Sheikh et al., 2015; Zahid nificant and one station was positively significant at 0.05 et al., 2017). significance level. Overall, the intensity of cold nights (TN10p) and cold days In cold days, the maximum negative trends were (TX10p) showed a consistent warming pattern across the observed in the northern mountainous region with the high- whole country. It has been reported that the increasing trend of est value at Chitral (−3.00 day/decade) station. The results cold nights and cold days could be the results of topographic are in agreement with the findings of (Sun et al., 2017; You factors, rapid urbanization, industrialization, deforestation, et al., 2017), who reported a significant negative trend of population expansion, or/and other anthropogenic factors −0.85 and −0.51 day/decade, respectively, in the HKH responsible for warming events (Balling et al., 2016; Roy region. Though they reported pronounced warming in south- et al., 2016). Furthermore, land cover and land use practices ern Pakistan, but still their trend magnitudes are lower than can also significantly affect the intensity and variability of tem- the present results. Abbas (2013), also found a decreasing perature extremes at a local scale (Safeeq et al., 2013). In ULLAH ET AL. 7

(a) (b) 3 Cold nights (TN10p) 3 Cold days (TX10p) 2 2 1 1 0 0 –1 –1 Cold days (d)

Cold nights (d) –2 –2 –3 –3

(c) 3 (d) 3 Warm nights (TN90p) Warm days (TX90p) 2 2 1 1 0 0 –1 –1 Warm days (d)

Warm nights (d) –2 –2 –3 –3

(e) 3 (f) 3 Frost days (FD) Summer days (SU25) 2 2 1 1 0 0 –1 –1 –2 –2 Summer days (d) –3 –3

(g) 3 (h) 3 Warmest day (TXx) Coldest day (TNn) 2 2 1 1 0 0 –1 –1

–2 Coldest day (°C) –2 Warmest day (°C) –3 –3 1980 1990 2000 2010 2016 (i) 3 Time (year) DTR 2 Annual mean anomaly 1 0 5 years moving mean –1 DTR (°C) Frost days (d) Linear –2 –3 1980 1990 2000 2010 2016 Time (year)

FIGURE 3 Annual mean anomalies of extreme temperature indices over the CPEC during 1980–2016; (a) cold nights (TN10p), (b) cold days (TX10p), (c) warm nights (TN90p), (d) warm days (TX90p), (e) FD, (f) SU (SU25), (g) warmest day (TXx), (h) coldest day (TNn), and (i) DTR (relative to 1980–2016 means) [Colour figure can be viewed at wileyonlinelibrary.com] addition, most cities of the study area have been expanded in Furthermore, the shifting in seasonal circulations and terms of population and urbanization over the last few land–sea interaction may affect the long-term temperature of decades, which could result in urban heat island effect (Abbas, a region (Wang et al., 2012; 2017; Maenza et al., 2017). The 2013). Thus, the above-mentioned factors could result in a land–sea interaction processes and orographic factors are decreasing trend of cold nights (TN10p) and cold days also responsible for dynamic variations in local temperature (TX10p) and can lead to a warming trend over the country. (Dimri et al., 2015; Priya et al., 2015; 2017). In the present 8 ULLAH ET AL.

TABLE 3 Trends of temperature extreme indices over CPEC during urbanization. However, in this study, the stations with posi- 1980–2016 tive trends are located in the less populous and low urban- Indicator name ID Trend value Unit ized regions of the country where are positive signs for Cold nights TN10p −1.09* day/decade increasing trends of cold nights (TN10p) and cold days Cold days TX10p −1.58* day/decade (TX10p). Warm nights TN90p 1.49* day/decade Warm days TX90p 1.04* day/decade 4.4 | Warm nights and warm days FD FD −1.05* day/decade SU SU25 4.32* day/decade The spatial distribution of the trend of warm nights (TN90p) Warmest day TXx 0.45* C/decade and warm days (TX90p) is shown in Figure 5. The trend Coldest day TNn 0.51* C/decade analysis represents that most parts of the country experi- DTR DTR −0.35* C/decade enced a warming trend in terms of increasing warm nights (TN90p) and warm days (TX90p). In warm nights (TN90p), α *The trend is statistically significant at = .05 significance level. the central and southeastern parts of the country exhibited pronounced warming trend, while the northern mountainous study, the stations with the increasing trend of cold nights region showed a slight increasing trend. The highest positive (TN10p) and cold days (TX10p) are located in the coastal trend values were observed at Sargodha (3.00 day/decade) and mountainous regions of the country and are under the and Lahore (2.98 day/decade) stations. The results are in influence of western disturbance, complex topography and agreement with findings of previous studies (Klein et al., interaction of Arabian Sea with land in the adjacent cities. 2006; Sheikh et al., 2015; Sun et al., 2017; You et al., Therefore, these factors could decrease the intensity of tem- 2017). They reported significant positive trends of 1.70 and perature, which ultimately results in increasing trend of cold 1.80, 4–8, and 2.54 day/decade, respectively; however, their nights (TN10p) and cold days (TX10p) in the said regions. trend magnitudes are different than the present finding, Increased cloud covers can lead to cooling trend and it is which could be the results of the different time period and possible that the stations with positive trends of cold nights stations’ density. Irrespective of the whole country, some of (TN10p) and cold days (TX10p) are located in regions the stations in the northern mountainous region showed a where the cloud cover has increased (Revadekar et al., slight decreasing and an insignificant trend in warm nights 2013). Moreover, previous studies have found a positive cor- (TN90p). The results support the findings of Klein et al. relation between temperature and population in Pakistan (2006), who reported a decline in the number of warm nights (Abbas, 2013) and temperature and urbanization in China (−4 day/decade) in northern Pakistan. In contrast, Zahid and (Shi et al., 2015). They indicated that the local temperature Rasul (2012), indicated a warming trend in warm nights can be increased with population expansion and rapid which contradict the present results. In general, the trend of

(a) (b)

Cold nights (TN10p) Cold days (TX10p)

35°N

30°N

25°N

65°E 70°E 75°E 65°E 70°E 75°E

–3.00 - –2.00 –1.99 - –1.00 –0.99 - 0.00 0.01 - 1.00 1.01 - 2.00

FIGURE 4 Spatial distribution of trend of cold nights (TN10p) and cold days (TX10p) over the CPEC during 1980–2016; (a) cold nights (TN10p), (b) cold days (TX10p). Upwards pointing red triangles show increasing trends, downwards pointing blue triangles represent decreasing trends (unit: day/decade). The white dots indicate statistically significant trends at α = .05 significance level [Colour figure can be viewed at wileyonlinelibrary.com] ULLAH ET AL. 9

(a) (b) Warm nights (TN90p) Warm days (TX90p)

35°N

30°N

25°N

65°E 70°E 75°E 65°E 70°E 75°E

–3.00 - –2.00 –1.99 - –1.00 –0.99 - 0.00 0.01 - 1.00 1.00 - 2.00 2.01 - 3.00

FIGURE 5 Spatial distribution of trend of warm nights (TN90p) and warm days (TX90p) over the CPEC during 1980–2016; (a) warm nights (TN90p), (b) warm days (TX90p). Upwards pointing red triangles show increasing trends, downwards pointing blue triangles represent decreasing trends (unit: day/decade). The white dots indicate statistically significant trends at α = .05 significance level [Colour figure can be viewed at wileyonlinelibrary.com] warm nights (TN90p) was statistically significant at 30 sta- of which 18 stations were positively significant and two sta- tions out of which 28 stations were positively significant and tions were negatively significant at 0.05 significance level. two stations were negatively significant at 0.05 significance The possible factors which intensify the frequency of level. warm events (warm nights and warm days) may be due to The spatial distribution of warm days (TX90p) shows local urban heat island effects (You et al., 2008). In recent that the southern belt of the country experienced notable years, the population has been expanded in most of the warming trend with maximum positive trends at Chhor Pakistani cities which would increase the intensity of tem- (2.88 day/decade) and Jiwani (2.55 day/decade) stations. perature and extreme events. In addition, rapid urbanization The results concur the findings of Khan et al. (2018). They and land use practices can significantly affect the local sur- reported a pronounced warming trend in the number of face warming pattern (Feddema et al., 2005; Changadeya warm days (TX90p) with an increasing trend of (4.41 day/ and Kambewa, 2012). Many cities have been urbanized due decade) over the target region. The results also concur the to the development of large industries and migration of rural findings of Sun et al. (2017) and You et al. (2017) who people to urban areas for better education and job opportuni- found an increasing trend in warm days (TX90p) at the rate ties. It also resulted in the shortage of public transport (buses of 1.23 and 1.26 day/decade, respectively. Moreover, the or rail) and increase in the number of personal transport northern mountainous region exhibited a slight increase in facilities such as cars, motorbikes, and auto rickshaws the number of warm days (TX90p) during the study period. (Abbas, 2013). The expanded urbanization and more utiliza- Regardless of the entire country, few stations located in the tion of individual vehicles may enhance the emission of coastal belt and central parts of the country exhibited a slight greenhouse gasses, which ultimately increase the local tem- decreasing and non-significant trend in the number of warm perature and warming trend in the region (Kalnay and Cai, days (TX90p). The results contradict the findings of Sun 2003; Li and Xue, 2010; Safeeq et al., 2013). Moreover, et al. (2017) and Zahid et al. (2017). They reported a pro- several agricultural practices such as increased irrigation and nounced warming trend in the number of warm days different cropping patterns have a significant positive effect (TX90p) in southern Pakistan. However, the result concurs on long-term temperature trends in the Indian sub-continent with the findings of Abbas (2013), who found a decreasing (Roy et al., 2007). Though, Pakistan is an agro-based coun- trend of −6.42 day/decade in the number of warm days try and major part of the land along the Indus River is used (TX90p) in the target region. Though the slope of trend for agricultural purposes. Therefore, the agricultural prac- determined by the previous study is higher than the present tices of local farmers cannot be neglected in exacerbating study, which could be the outcome of less number of sta- the trend of long-term temperature, which could also tions and different study period. Overall, the trend of warm increase the number of warm nights (TN90p) and warm days days (TX90p) was statistically significant at 20 stations, out (TX90p) in the study region. 10 ULLAH ET AL.

In the present study, some of the stations located in the northern and southwestern regions, while the reaming parts northern, central, and southern regions of the country exhib- of the country showed a slight decrease in the number of ited decreasing trends in warm nights (TN90p) and warm FD. Because most parts of the country have a tropical and days (TX90p). Related studies suggested that factors affect- subtropical climate where FD occur rarely (Sheikh et al., ing the frequency of warm nights (TN90p) and warm days 2015). The maximum trend of FD was recorded at Astore (TX90p) may include; the fluctuations in regional atmo- (−4.037 day/decade), Dir (−4.199 day/decade), Murree spheric circulations, topographic features, and thermody- (−5.00 day/decade), and Zhob (−4.00 day/decade) stations. namic feedback process (You et al., 2008; Wang et al., Sun et al. (2017) and You et al. (2017) also reported a 2011; 2017; Dimri et al., 2015; Sun et al., 2016; Maenza warming trend over the HKH region during the last century. et al., 2017; Priya et al., 2017). The study area has a diverse They indicated that the number of FD has been decreased topography with an exposed geographical location to many over Hindu Kush Himalayas at the rates of −3.36 and −4.32 local and regional weather systems. The stations with day/decade, respectively. Regardless of the whole country, decreasing trend are located in the westerlies and monsoon two stations (Parachinar and Dalbandin) located at high ele- core regions which influence the long-term temperature vation exhibited a significant positive trend in FD, which trends of the region (Iqbal and Athar, 2018). Subash and contradict the findings of the regional and country level Sikka (2014) found a negative correlation between tempera- studies (Deshpande et al., 2016; Shahid et al., 2016; Sun ture and monsoonal rainfall in adjoining (northeastern) et al., 2016). However, Sheikh et al. (2015)) reported a sig- regions of Pakistan. In recent years, a significant reduction nificant decrease in the number of FD in major parts of has been reported in long-term temperature and extreme Himalayan Region. Moreover, the results of the MK test warm events in summer monsoon and winter westerlies core show that the trend of FD was significant at three stations, regions of Pakistan (Iqbal et al., 2016; Khan et al., 2018), out of which two stations exhibited significant positive which could be linked with these seasonal weather systems trends while one station exhibited a significant negative (Feddema et al., 2005; Wang et al., 2011; Gardelle trend at 0.05 significance level. et al., 2012). The spatial distribution of SU (SU25) revealed that the whole country experienced a warming trend. The magnitude of positive trend was considerably higher in the northern and 4.5 | FD and SU southwestern parts with maximum positive trend at Balakot, Figure 6 shows the annual count of FD and SU (SU25) in Dalbandin, Kakul, Kohat, Murree, Muzaffarabad, Panjgur, the study area. The analysis indicates that the number of and Zhob stations at the rates of 6.28, 8.18, 7.18, 6.67, 6.67, frost (summer) days has been decreased (increased) over the 6.22, 7.21, and 7.00 day/decade, respectively. The results entire region. The spatial analysis of trend shows that the are consistent with the findings of previous studies (Sun decreasing trend of FD was obvious in the higher elevated et al., 2017; You et al., 2017; Zahid et al., 2017). Sun et al.

(a) (b) Frost days (FD) Summer days (SU25)

35°N

30°N

25°N

65°E 70°E 75°E 65°E 70°E 75°E

-5.00 - -3.00 -2.99 - -1.00 -0.99 - 0.00 0.01 - 2.00 2.01 - 5.00 5.01 - 9.00

FIGURE 6 Spatial distribution of trend of FD and SU (SU25) over the CPEC during 1980–2016; (a) FD, (b) SU (SU25). Upwards pointing red triangles show increasing trends, downwards pointing blue triangles represent decreasing trends (unit: day/decade). The white dots indicate statistically significant trends at α = .05 significance level [Colour figure can be viewed at wileyonlinelibrary.com] ULLAH ET AL. 11

(2017) observed the same pattern of SU over the target the maximum negative of warmest day (TXx) trend over the region with a significant increase of 6.74 day/decade. Simi- study period; however, the trends of these stations were statisti- larly, Sheikh et al. (2015) also detected a positive trend cally not significant. Generally, the positive trend of the warm- (4.00 day/decade) in SU (SU25) over Pakistan. However, est day (TXx) was significant at five stations at 0.05 the results of this study contradict the findings of Khan et al. significance level. (2018) who reported a decreasing trend in the frequency of In coldest day (TNn), the trend was noticeably increased SU (SU25) over northern Pakistan. Moreover, Abbas (2013) in southern and southeastern parts of the country. The maxi- also detected a negative trend of (−4.73 day/decade) in the mum positive trend values of the coldest day (TNn) were central part of Pakistan (Multan), which also contradict the observed at Badin, Bunji, Chhor, Karachi, Khanpur, Khuz- findings of this study. Though the above studies are different dar, Rohri, and Shaheed Benazirabad stations at the rates of from the present study in terms of data type and study 0.52, 0.62, 0.53, 0.69, 0.61, 0.71, 0.74, and 0.52 C/decade, period, which could affect the outcomes. In SU (SU25), respectively. The results are consistent with the findings of Parachinar was the only station which showed a sharp Klein et al. (2006) and Sun et al. (2017). They stated that decreasing and non-significant trend. According to the MK the trend of the coldest day (TNn) has been increased at the test, the positive trend of SU (SU25) is statistically signifi- rates of 0.42 and 0.73 C/decade, respectively, in the study cant at 14 stations. region. Despite the whole, some of the stations located in The possible factors responsible for decreasing/ increas- central and the northern mountainous regions exhibited neg- ing trend of FD/SU (SU25) over the target region could be ative but non-significant trends. Related studies also reported the results of snow/ice albedo feedback and changes of envi- similar results. Abbas (2013) found a decreasing trend of ronmental elements such as cloud amount, specific humidity, −0.62 C/decade in the coldest day (TNn) over central parts and land use changes (You et al., 2008). Moreover, the of Pakistan. Similarly, Sheikh et al. (2015) detected a nega- emissions of greenhouse gasses and land use changes also tive trend of 0.10 C/decade in coldest day (TNn) over intensify the long-term temperature trends which may results northern Pakistan. Based on the MK test results, 10 stations in decreasing/ increasing frequency of FD/SU (SU25) showed a significant positive trend at 0.05 significance (Feddema et al., 2005; Mahlstein and Knutti, 2010). Irre- level. spective of the entire country, few stations located at high Moreover, the trends in the DTR vary significantly elevation exhibited a positive/negative trend in FD/SU across the country. The spatial analysis of DTR shows that (SU25) during the study period. The said stations are located most of the stations exhibited significant sharp decreasing in mountainous regions with complex topography which trend throughout the country. However, the negative trend may affect the long-term temperature trend at a local scale was more obvious in southern and southeastern regions. The (Iqbal and Athar, 2018). The unexpected trend of FD and maximum negative trend was found at Chhor, Karachi, SU (SU25) at these stations could be the outcome of sparse Khuzdar, Lahore, Rohri, Sargodha, and Shaheed Benazira- ’ station s density, inaccessibility, and poor data management bad stations at the rates of −0.77, −0.66, −0.50, −0.56, of meteorological data at high elevation (Pellicciotti et al., −0.71, −0.57, and −0.54 C/decade, respectively. The et al 2012; Ren ., 2017). results are consistent with the findings of previous studies. Abbas (2013) reported a decrease of −0.47, Khan et al. 4.6 | Warmest day, coldest day, and DTR (2018) −0.15, Sun et al. (2017) −0.11, Klein et al. (2006) The spatial distribution of trends in the warmest day (TXx), −0.12, and You et al. (2017) −0.20 C/decade in DTR. Sim- coldest day (TNn), and DTR are shown in Figure 7. The analy- ilar results were reported by Khan et al. (2018) with sis indicated that the extreme values of temperature for the −0.20 C/decade. Despite the entire country, few stations warmest day (TXx) and coldest days (TNn) were increased located in the central and northern mountainous region over the whole country. The trend of the warmest day (TXx) showed a positive trend in DTR. Sheikh et al. (2015) also was sharply increased in the northern mountainous region, found an increasing trend in DTR in the northern Pakistan while the central and southern regions exhibited a slight posi- with 0.80 C/decade. Overall, the trends of DTR were sig- tive and insignificant trend. The highest positive trend of the nificant at 24 stations, out of which 19 stations exhibited a warmest day was detected at Balakot (0.65 C/decade), Bunji significant negative trend while 5 stations showed a signifi- (0.71 C/decade), Chitral (0.57 C/decade), and Kakul cant positive trend. (0.62 C/decade) stations. The results support the findings of The trend in extreme temperature of the warmest day Sheikh et al. (2015) and Sun et al. (2017). They found an obvi- (TXx) and coldest day (TNn) can be influenced by several ous increasing trend of the warmest day (TXx) at the rates of factors, including complex topography, land cover, local 0.80 and 0.29 C/decade, respectively. In contrast, Abbas heat fluxes, and regional atmospheric circulations (You (2013), reported a negative trend of −0.13 C/decade in the et al., 2008; Li et al., 2012a; Yan et al., 2015; Xu et al., central parts of Pakistan; however, this trend represents a few 2018). Moreover, anthropogenic activities such as deforesta- stations. Similarly, Parachinar and Hyderabad stations exhibited tion, urbanization, extensive use of fossil fuels, and other 12 ULLAH ET AL.

(a) (b)

Warmest day (TXx) Coldest day (TNn)

35°N

30°N

25°N

65°E 70°E 75°E 65°E 70°E 75°E

(c) DTR

35°N

30°N

25°N

65°E 70°E 75°E

-0.8 - -0.2 -0.1 - 0.00 0.1 - 0.2 0.3 - 0.4 0.5 - 0.8

FIGURE 7 Spatial distribution of trend of warmest day (TXx), coldest day (TNn), and DTR over the CPEC during 1980–2016; (a) warmest day (TXx), (b) coldest day (TNn), and (c) DTR. Upwards pointing red triangles show increasing trends, downwards pointing blue triangles represent decreasing trends (unit: C/decade). The white dots indicate statistically significant trends at α = .05 significance level [Colour figure can be viewed at wileyonlinelibrary.com] human actions can also enhance the long-term temperature 2013). In the present study, the warming trends in minimum and can lead to warming of the warmest day (TXx) and cold- temperature indices were detected steeper than those for est days (TNn) (Mahlstein and Knutti, 2010; Fall et al., maximum temperature across the country. In addition, the 2011; Li et al., 2012b; You et al., 2017). The stations with stations with increasing trends of DTR are located at high negative trends of warmest day (TXx) and coldest day elevation, where the differences between extreme maximum (TNn) are located in westerlies dominated region with multi- and extreme minimum temperatures are high which may faceted mountainous topography, which may reduce the result in a sharp positive trend of DTR (Jhajharia and Singh, warming trend and the number of the warmest day (TXx) 2011). Moreover, rapid urbanization, deforestation, and land and coldest day (TNn) (Dimri et al., 2015). In addition, the use practices may also affect the trend of DTR (You et al., decrease in DTR can be attributed to the increases of cloud 2008). In addition, it has been indicated that DTR is inde- cover, precipitation and soil moisture (Sayemuzzaman et al., pendent of internal climate variations; however, it provides 2015). The decreasing trend of DTR is mostly associated supplementary information for the detection and attribution with the sharper trend in minimum temperature indices than of climate change in similar ways as spatial characteristics of maximum temperature indices (Shahid et al., 2012; Abbas, climate change (Braganza et al., 2003; Shahid et al., 2012). ULLAH ET AL. 13

The reduction of DTR in most parts of the country reveals increase in wind speed in the southern region. Similarly, the that the impact of global warming induced climate change is maximum geopotential height differences (approximately evident in the study region (Khan et al., 2018). 50 gpm) are observed in the northern mountainous region, while the lowest geopotential height differences (approxi- 4.7 | Changes in large-scale atmospheric circulation mately −10 gpm) are detected in the southern region of the (wind field, geopotential height, air temperature) study area. This indicates that the increase (decrease) in geo- potential height with low (high) wind speed may be associ- Changes in surface variables are usually affected by changes ated with increasing (decreasing) trend of temperature and in atmospheric fields such as wind speed, air temperature, ultimately affect the occurrence of temperature extremes in geopotential height, relative, and specific humidity (Guo the region (Nasrallah et al., 2004; Sayemuzzaman et al., et al., 2016b; Li et al., 2018). There do exist positive and negative feedbacks between these atmospheric and surface 2015; Shi et al., 2018; Xu et al., 2018). The strong winds meteorological fields, both in time and space. Among these from the Indian Ocean are moving towards the coastal area, fields, wind speed, air temperature, and geopotential height which may affect the long-term temperature at a local scale are often used for assessing and linking the changes in sur- (Priya et al., 2017; Pathak et al., 2017a). According to face temperature in several studies (Sun et al., 2016; Ren Pathak et al. (2017b), the strong winds are capable to trans- et al., 2017). To investigate the indirect influence of circula- port water vapours from the ocean to the adjacent coastal tion change in the trends of maximum and minimum temper- areas which may affect the local temperature and result in a atures and related extremes, we calculated the differences in cooling trend and vice versa. In addition, the mean differ- – – wind speed and air temperature from ERA-Interim reanalysis ence of air temperature between 1980 1997 and 1998 2016 for the two halves of the data period, that is, 1980–1997 and shows that the northwestern regions experienced an obvious 1998–2016 (Dee et al., 2011; Xu et al., 2018). The former increase in air temperature while the southeastern region is was subtracted from the latter to detect changes in circula- dominated by a decrease in air temperature which can also tion and air temperature between the two periods (You et al., influence the local temperature and the occurrence of tem- 2011; Shi et al., 2018). The domain of the selected region perature extremes (Khan et al., 2008; Grotjahn et al., 2016; ranges from 0–40N and 55–115E. Figure 8 shows the Ren et al., 2017). The present study also reported increasing mean difference of wind (vectors) and geopotential height (decreasing) trends of cold (warm) events in the southern (shaded) at 850 hPa (plot a) and air temperature (plot b) (northern) parts of the country, which are consistent with the between 1980–1997 and 1998–2016. The differences in geo- circulation and air temperature patterns described here. It is potential height and wind speed at 850 hPa between interesting to see that the all atmospheric fields have shown 1980–1997 and 1998–2016 indicate a decrease in wind field a dipole pattern over the northern and southern parts of the at the northern mountainous region of the study area and study area. Investigating the dynamics and mechanism of

(a) (b) 40°N

30°N

20°N

10°N

EQ 60°E 70°E 80°E 90°E 100°E 110°E 60°E 70°E 80°E 90°E 100°E 110°E

–30 –20 –10 0 10 20 30 40 50 –0.2 0 0.2 0.4 0.6 0.8 1

FIGURE 8 Difference of wind speed and geopotential height at 850 hPa (a) and air temperature (b) between 1980–1997 and 1998–2016 [Colour figure can be viewed at wileyonlinelibrary.com] 14 ULLAH ET AL. this dipole pattern is currently beyond the scope of the cur- processes (Ragettli et al., 2016). The region receives heavy rent study, which will be addressed in a separate study. snow in winter season due to mid-latitude western distur- bance (Iqbal and Athar, 2017; Hunt et al., 2018), while 4.8 | Elevation-dependent warming intense precipitation in summer season due to South Asian monsoon systems (Wang et al., 2011; Priya et al., 2017; In recent years, EDW becomes an interesting area of Wang et al., 2017). Recent studies reported that the spatio- research for understanding the climate change phenomenon. temporal variability of temperature and precipitation in Related studies revealed that warming at higher altitudes is northern Pakistan has significant effects on water availability more obvious than that at lower altitudes (You et al., 2010; of the Indus River network (Hartmann and Buchanan, 2014; Guo et al., 2016a; Li et al., 2017; Sun et al., 2017; You Ahmad et al., 2015). The Indus River and its tributaries face et al., 2017). However, many studies have also stated that water scarcity in 1 year and catastrophic floods in the other there are no clear evidence of EDW phenomenon (You year (Ashiq et al., 2010; Hassan and Ansari, 2015). The et al., 2008). The ambiguity in the EDW phenomenon could country has faced severe floods and drought disasters in the be the outcome of sparse station’s density, inaccessibility, last few decades, which could be the results of these poor or inadequate data management of meteorological data phenomena. (Pellicciotti et al., 2012; Ren et al., 2017). In the present The present study found a pronounced a warming in study, several indices showed high spatial variability most of the temperature extreme indices over the study between mountainous regions and low-lying areas. There- region, which may have potential impacts on local and fore, we tried to assess the possible effects of elevation on regional water resources. The increase (decrease) trend of the spatiotemporal variability of temperature extremes in the warm days, SU, warmest day, and DTR (cold days, FD) has study area. a significant effect on evapotranspiration, and ultimately on Figure 9 shows the connection between the stations’ ele- agriculture sector of the country (Iqbal et al., 2016). More- vation and trends of temperature extremes in the study area. over, the warming trend can also result in melting of snow The results indicate that the trend of temperature extreme and glaciers which provide a large fraction of water for indices exhibited a mixture of significant positive and nega- drinking, irrigation and other domestic purposes in low-lying tive correlation with elevation. The temperature indices regions. However, it has also been reported that the exces- based on maximum temperature showed a significant posi- sive melting of snow and glaciers in the HKH region can tive relationship with elevation except for cold days overflow the rivers and result in catastrophic flooding in the (TX10p). The trends of warm days (TX90p), SU (SU25), downstream areas (ur Rahman and Khan, 2011; Ahmad warmest day (TXx), and DTR were significantly increased et al., 2015). The floods of 2010 and 2015 witnessed the with an increase in elevation. Similar results were reported disastrous phenomenon coupling with the intensive mon- soon and orographic precipitations. Similarly, the decreasing by previous studies (Zahid and Rasul, 2012; Revadekar (increasing) trends of cold nights (warm nights) can modu- et al., 2013). In contrast, the indices based on minimum tem- late the melting of snow and glaciers which may negatively perature showed significant negative correlations with eleva- affect water resources. As a result, the country could face a tion except for cold nights (TN10p). The index of cold days scarcity of water and doughty condition in the following (TX10p) along with other minimum temperature based indi- years (Gardelle et al., 2012; Kääb et al., 2012). Based on the ces; warm nights (TN90p), FD, coldest day (TNn), exhibited above findings, it has been concluded that the country is vul- a significant negative correlation with stations’ elevations. nerable to both floods and droughts due to high spatiotempo- The results are in agreement with the findings of previous ral variability of temperature and precipitation extremes in studies (Revadekar et al., 2013; Ren et al., 2017; Sun et al., HKH region. However; all these facts require more in-depth 2017). Cold nights’ index is the only minimum temperature studies in order to investigate the natural and anthropogenic based index, which showed a slight positive relation with drivers of EDW and its impacts on water resources in the elevation. Though several studies have reported evidence of study region. EDW in the study area and the region; however, this phe- nomenon is still ambiguous and the drivers of the EDW need to be thoroughly investigated at local and regional scales. 5 | CONCLUSIONS The northern mountainous region of Pakistan is a part of HKH ranges (Ren et al., 2017). The region is a hub of gla- The present study assessed the spatiotemporal changes in ciers and snow cover which fulfils large proportion of the nine temperature extreme indices over the CPEC during country’s water and power generation demands (Iqbal and 1980–2016. The time series of annual mean anomalies indi- Athar, 2018). However, the impacts of temperature extremes cates that the trends of warm nights (TN90p), warm days on hydrological balance and river flow at high altitude (TX90p) and SU (SU25), warmest day (TXx), and coldest remain poorly understood because of high meteorological day (TNn) indices have significantly increased over the variability, physical inaccessibility, and the complex interac- study period. Moreover, cold nights (TN10p), cold days tion between climate, cryosphere, and hydrological (TX10), FD, and DTR exhibited significant negative trends ULLAH ET AL. 15

(a)2 (b) 2 Cold nights (TN10p) Cold days (TX10p)

0 0

–2 –2 Trend (day/decade) Trend (day/decade) Trend

y = 5.9E–05x –1.7 R2= 0.001 y = –0.00087x –0.9 R2 = 0.279 –4 –4

(c)4 (d) 4 Warm nights (TN90p) Warm days (TX90p) 3 3 2 2 1 1 0 0 –1 –1 Trend (day/decade) Trend (day/decade) Trend –2 –2 y = –0.0009x + 2.3 R2 = 0.326 y = 0.0001x + 0.99 R2 = 0.004 –3 –3 (e) (f) 8 10 Frost days (FD) Summer days (SU25)

4 5

0

0 –4 Trend (day/decade) Trend (day/decade) Trend

y = - 0.0007x + 0.11 R2 = 0.061 y = 0.00043x + 2.9 R2 = 0.015 –8 –5 (g) (h) 0.1 0.15 Warmest day (TXx) Coldest day (TNn) 0.1 0.05 0.05

0 0

Trend (°C/decade) Trend (°C/decade) Trend –0.05 y = 1.2E–05x + 0.0076 R2 = 0.175 y = –1.5E–05x + 0.038 R2 = 0.147 –0.05 –0.1 0 500 1000 1500 2000 2500 (i) 0.1 Elevation (m) DTR

0.05

0

–0.05 Trend (°C/decade) Trend

y = 2.7E–05x – 0.034 R2 = 0.322 –0.1 0 500 1000 1500 2000 2500 Elevation (m)

FIGURE 9 EDW trends of temperature extreme indices over the CPEC during 1980–2016; (a) cold nights (TN10p), (b) cold days (TX10p), (c) warm nights (TN90p), (d) warm days (TX90p), (e) FD, (f) SU (SU25), (g) warmest day (TXx), (h) coldest day (TNn), and (i) DTR [Colour figure can be viewed at wileyonlinelibrary.com] 16 ULLAH ET AL. during 1980–2016. The spatial analysis of percentile-based future studies should emphasis on the climatic and anthropo- indices represent that the frequency of cold nights (TN10) genic drivers responsible for changes in the trends of temper- and cold days (TX10) is evidently decreased while the fre- ature extremes over the target region. Moreover, detail quency of warm nights (TN90p) and warm days (TX90p) is studies should be conducted to investigate the mechanism of dominated by a pronounced positive trend over the whole EDW in the study area. country. Similarly, the spatial distribution of absolute value based indices indicate that the trends of SU (SU25), warmest day (TXx), and coldest day (TNn) are increasing, while the ACKNOWLEDGEMENTS trends of FD and DTR are decreased in the target region. This study was supported by National Key R&D Program of The results depict that the spatial variability of the trends is China (2017YFA0603804), National Natural Science Foun- high in northern mountains, southern coastal belt, and south- dation of China (41771069), Jiangsu Natural Science Funds eastern region of the country. This spatial variability could for Distinguished Young Scholar “BK20140047.” This be the outcome of topographic factors, land use practices, study was also funded by “The Priority Academic Program deforestation, urbanization, industrialization, and/or other Development of Jiangsu Higher Education Institutions” anthropogenic factors responsible for warm events. The (PAPD). The authors would also like to acknowledge decrease (increase) in cold (warm) events indicated that the Pakistan Meteorological Department (PMD) for providing impact of global warming induced climate change is obvious temperature data. in the study region. The pronounced warming trend in most of the temperature extreme indices over the study region ORCID would have potential impacts on local and regional water Safi Ullah https://orcid.org/0000-0002-2328-8321 resources. The rapid increase in warming trend may result in Qinglong You https://orcid.org/0000-0002-5329-9697 melting of snow and glaciers, which provide large a fraction of water for drinking, irrigation and other domestic purposes REFERENCES in low-lying regions. However, the excessive melting of Abbas, F. (2013) Analysis of a historical (1981–2010) temperature record of the snow and glaciers in the northern mountainous region can Punjab Province of Pakistan. Earth Interactions, 17(15), 1–23. overflow the rivers and result in catastrophic flooding in the Abid, M. and Ashfaq, A. (2016) CPEC: Challenges and Opportunities for Paki- – downstream areas. The variations in large-scale atmospheric stan. Pakistan Vision, 16(2), 144 169. Ahmad, I., Tang, D., Wang, T., Wang, M. and Wagan, B. (2015) Precipitation circulation derived from ERA-Interim reanalysis grids exhib- trends over time using Mann–Kendall and Spearman’s rho tests in Swat ited a weak (strong) wind pressure, increasing (decreasing) River basin, Pakistan. Advances in Meteorology, 43(11), 1–15. geopotential height and rapid warming (cooling) trend over Almazroui, M., Islam, M.N., Dambul, R. and Jones, P.D. (2014) Trends of tem- perature extremes in Saudi Arabia. International Journal of Climatology, the northern (southern) region, which may contribute to the 34(3), 808–826. changes in temperature extremes in Pakistan. It is interesting Ashiq, M.W., Zhao, C., Ni, J. and Akhtar, M. (2010) GIS-based high-resolution to see that the all atmospheric fields have shown a dipole spatial interpolation of precipitation in mountain-plain areas of Upper Pakistan for regional climate change impact studies. Theoretical and Applied pattern over the northern and southern parts of the study Climatology, 99(3–4), 239–253. area. Investigating the dynamics and mechanism of this Asmat, U. and Athar, H. (2017) Run-based multi-model interannual variability dipole pattern is currently beyond the scope of the current assessment of precipitation and temperature over Pakistan using two IPCC AR4-based AOGCMs. Theoretical and Applied Climatology, 127(2), study, which will be addressed in a separate study. This 1, 1–16. study also assessed the EDW phenomenon in the region. Asmat, U., Athar, H., Nabeel, A. and Latif, M. (2017) An AOGCM based assess- The relationship between the stations’ elevation and trends ment of interseasonal variability in Pakistan. Climate Dynamics, 50(1), 1–25. of temperature extremes was analysed during 1980–2016. Balling, R.C., Kiany, M.S.K. and Sen, R.S. (2016) Anthropogenic signals in Iranian extreme temperature indices. Atmospheric Research, 169(1), 96–101. The analysis indicates that the trends of both percentile and Berardy, A. and Chester, M.V. (2017) Climate change vulnerability in the food, absolute based indices show a mixture of positive and nega- energy, and water nexus: concerns for agricultural production in Arizona and – tive correlation with stations’ elevation in the study area. its urban export supply. Environmental Research Letters, 12(3), 1 13. Braganza, K., Karoly, D.J., Hirst, A.C., Mann, M.E., Stott, P., Stouffer, R.J. and The trends of cold nights (TN10p), warm days (TX90p), SU Tett, S.F.B. (2003) Simple indices of global climate variability and change: (SU25), warmest day (TXx), and DTR showed a significant part I—variability and correlation structure. Climate Dynamics, 20(5), – positive correlation with increasing stations’ elevation. On 491 502. Casella, G. and Berger, R.L. (2002) Statistical Inference, 2nd ed. Belmont, CA: the other hand, the trends of cold days (TX10p), warm Duxbury Thomson Learning Press. nights (TN90p), FD, and coldest day (TNn) significantly Changadeya, W. and Kambewa, D. (2012) Impact of urbanization and land use decrease with the increase of stations’ elevation. Though, the changes on climate. International Journal of Physical and Social Sciences, 2(4), 32–48. present study provided details on the spatiotemporal trends Cheng, C.S., Auld, H., Li, Q. and Li, G. (2012) Possible impacts of climate of temperature extremes and their relationship with elevation change on extreme weather events at local scale in south-central Canada. Cli- in the region. But still, the climatic/anthropogenic factors of matic Change, 112(3–4), 963–979. Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., temperature extremes and mechanism of EDW are ambigu- Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., ous in the region. Therefore, this study recommends that Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., ULLAH ET AL. 17

Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hartmann, H. and Buchanan, H. (2014) Trends in extreme precipitation events in Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., the Indus River basin and flooding in Pakistan. Atmosphere-Ocean, 52(1), Matricardi, M., Mcnally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B. 77–91. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.N. and Vitart, F. Hassan, S.A. and Ansari, M.R.K. (2015) Hydro-climatic aspects of Indus River (2011) The ERA-Interim reanalysis: configuration and performance of the flow propagation. Arabian Journal of Geosciences, 8(12), 10977–10982. data assimilation system. Quarterly Journal of the Royal Meteorological Herring, S.C., Hoerling, M.P., Kossin, J.P., Peterson, T.C. and Stott, P.A. (2015) Society, 137(656), 553–597. Explaining extreme events of 2014 from a climate perspective. Bulletin of Del Río, S., Anjum Iqbal, M., Cano-Ortiz, A., Herrero, L., Hassan, A. and the American Meteorological Society, 96(12), 1–172. Penas, A. (2013) Recent mean temperature trends in Pakistan and links with Hunt, K.M.R., Turner, A.G. and Shaffrey, L.C. (2018) The evolution, seasonality teleconnection patterns. International Journal of Climatology, 33(2), and impacts of western disturbances. Quarterly Journal of the Royal Meteo- 277–290. rological Society, 144(710), 278–290. Deshpande, N.R., Kothawale, D.R. and Kulkarni, A. (2016) Changes in climate IPCC. (2013) Climate Change 2013: The Physical Science Basis. Cambridge extremes over major river basins of India. International Journal of Climatol- and New York, NY: Cambridge University Press. ogy, 36(14), 4548–4559. Iqbal, M.F. and Athar, H. (2017) Variability, trends, and teleconnections of Dikbas, F., Firat, M., Koc, A.C. and Gungor, M. (2010) Homogeneity test for observed precipitation over Pakistan. Theoretical and Applied Climatology, – Turkish temperature series. Balwois, 25(8), 1 5. 10(17), 1–20. Dimri, A.P., Niyogi, D., Barros, A.P., Ridley, J., Mohanty, U.C., Yasunari, T. Iqbal, M.F. and Athar, H. (2018) Validation of satellite-based precipitation over and Sikka, D.R. (2015) Western disturbances: a review. Reviews of Geophys- diverse topography of Pakistan. Atmospheric Research, 201(4), 247–260. – ics, 53(2), 225 246. Iqbal, M.A., Penas, A., Cano-Ortiz, A., Kersebaum, K.C., Herrero, L. and del Donat, M.G., Alexander, L.V., Yang, H., Durre, I., Vose, R., Dunn, R.J.H., Río, S. (2016) Analysis of recent changes in maximum and minimum tem- Willett, K.M., Aguilar, E., Brunet, M., Caesar, J., Hewitson, B., Jack, C., peratures in Pakistan. Atmospheric Research, 168(9), 234–249. Klein Tank, A.M.G., Kruger, A.C., Marengo, J., Peterson, T.C., Renom, M., Irshad, M., Xin, Q. and Arshad, H. (2015) One belt and one road: dose Oria Rojas, C., Rusticucci, M., Salinger, J., Elrayah, A.S., Sekele, S.S., China–Pakistan Economic Corridor benefit for Pakistan’s economy? Journal Srivastava, A.K., Trewin, B., Villarroel, C., Vincent, L.A., Zhai, P., of Economic and Sustainable Development, 6(24), 1–8. Zhang, X. and Kitching, S. (2013) Updated analyses of temperature and pre- Islam, M.N., Almazroui, M., Dambul, R., Jones, P.D. and Alamoudi, A.O. cipitation extreme indices since the beginning of the twentieth century: the (2014) Long-term changes in seasonal temperature extremes over Saudi Ara- HadEX2 dataset. Journal of Geophysical Research: Atmospheres, 118(5), bia during 1981–2010. International Journal of Climatology, 35(7), 2098–2118. 1579–1592. Donat, M.G., Peterson, T.C., Brunet, M., King, A.D., Almazroui, M., Kolli, R. Javaid, U. (2016) Assessing CPEC: potential threats and prospects. Journal of K., Boucherf, D., Al-Mulla, A.Y., Nour, A.Y., Aly, A.A., Nada, T.A.A., the Research Society of Pakistan, 53(2), 254–269. Semawi, M.M., Al Dashti, H.A., TG, S., El Fadli, K.I., Muftah, M.K., Dah Jhajharia, D. and Singh, V.P. (2011) Trends in temperature, diurnal temperature Eida, S., Badi, W., Driouech, F., El Rhaz, K., Abubaker, M.J.Y., Ghulam, A. range and sunshine duration in northeast India. International Journal of Cli- S., Erayah, A.S., Ben, M.M., Alabdouli, W.O., Al Dhanhani, J.S. and Al matology, 31(9), 1353–1367. Shekaili, M.N. (2014) Changes in extreme temperature and precipitation in Kääb, A., Berthier, E., Nuth, C., Gardelle, J. and Arnaud, Y. (2012) Contrasting the Arab region: long-term trends and variability related to ENSO and NAO. patterns of early twenty-first-century glacier mass change in the Himalayas. International Journal of Climatology, 34(3), 581–592. Nature, 488(7412), 495–498. Fall, S., Watts, A., Nielsen-Gammon, J., Jones, E., Niyogi, D., Christy, J.R. and Kalnay, E. and Cai, M. (2003) Impact of urbanization and land-use change on Pielke, R.A. (2011) Analysis of the impacts of station exposure on the climate. Nature, 423(5), 528–531. U.S. historical climatology network temperatures and temperature trends. Kendall, M.G. (1955) Rank Correlation Methods, 2nd edition. Oxford: Hafner. Journal of Geophysical Research: Atmospheres, 116(14), 1–15. Khan, T.M.A., Khan, F.A. and Jilani, R. (2008) Sea surface temperature variabil- Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja, L.E., Meehl, G. ity along Pakistan coast and its relation to El Niño–Southern Oscillation. A. and M, W. (2005) The importance of land-cover change in simulating Journal of Basic and Applied Sciences, 4(2), 67–72. future climates. Science, 310(5754), 1674–1678. Khan, N., Shahid, S., Bin, I.T. and Wang, X.-J. (2018) Spatial distribution of Frías, M.D., Mínguez, R., Gutiérrez, J.M. and Méndez, F.J. (2012) Future unidirectional trends in temperature and temperature extremes in Pakistan. regional projections of extreme temperatures in Europe: a nonstationary sea- Theoretical and Applied Climatology, 18(6), 1–15. sonal approach. Climatic Change, 113(2), 371–392. Klein Tank, A.M.G., Peterson, C.T., Quadir, D.A., Dorji, S., Zou, X., Tang, H., Gadiwala, M.S., Burke, F., Alam, M.T., Nawaz-ul-huda, S. and Azam, M. Santhosh, K., Joshi, U.R., Jaswal, A.K., Kolli, R.K., Sikder, A.B., (2013) Oceanity and continentality climate indices in Pakistan. Malaysian Journal of Society and Space, 9(4), 57–66. Deshpande, N.R., Revadekar, J.V., Yeleuova, K., Vandasheva, S., Galarneau, T.J., Hamill, T.M., Dole, R.M. and Perlwitz, J. (2012) A multiscale Faleyeva, M., Gomboluudev, P., Budhathoki, K.P., Hussain, A., Afzaal, M., analysis of the extreme weather events over western Russia and northern Chandrapala, L., Anvar, H., Amanmurad, D., Asanova, V.S., Jones, P.D., Pakistan during July 2010. Monthly Weather Review, 140(5), 1639–1664. New, M.G. and Spektorman, T. (2006) Changes in daily temperature and Gardelle, J., Berthier, E. and Arnaud, Y. (2012) Slight mass gain of Karakoram precipitation extremes in central and South Asia. Journal of Geophysical – glaciers in the early twenty-first century. Nature Geoscience, 5(4), 322–325. Research: Atmosphere, 111(D16), 1 8. Grinstead, C.M. and Snell, J.L. (1997) Introduction to probability. Simulation, Lau, W.K.M. and Kim, K.-M. (2012) The 2010 Pakistan flood and Russian heat 23(1), 510. wave: teleconnection of hydrometeorological extremes. Journal of Hydrome- Grotjahn, R., Black, R., Leung, R., Wehner, M.F., Barlow, M., Bosilovich, M., teorology, 13(1), 392–403. Gershunov, A., Gutowski, W.J., Gyakum, J.R., Katz, R.W., Lee, Y.Y., Lelieveld, J., Proestos, Y., Hadjinicolaou, P., Tanarhte, M., Tyrlis, E. and Lim, Y.K. and Prabhat. (2016) North American extreme temperature events Zittis, G. (2016) Strongly increasing heat extremes in the Middle East and and related large scale meteorological patterns: a review of statistical North Africa (MENA) in the 21st century. Climatic Change, 137(1–2), methods, dynamics, modeling, and trends. Climate Dynamics, 46(3–4), 245–260. 1151–1184. Li, Q. and Xue, Y. (2010) Simulated impacts of land cover change on summer Gu, H., Wang, G., Yu, Z. and Mei, R. (2012) Assessing future climate changes climate in the Tibetan Plateau. Environmental Research Letters, 5(1), 1–12. and extreme indicators in east and South Asia using the RegCM4 regional Li, B., Chen, Y., Chen, Z. and Li, W. (2012a) Trends in runoff versus climate climate model. Climatic Change, 114(2), 301–317. change in typical rivers in the arid region of northwest China. Quaternary Guo, D., Yu, E. and Wang, H. (2016a) Will the Tibetan Plateau warming depend International, 282(12), 87–95. on elevation in the future? Journal of Geophysical Research: Atmospheres, Li, B., Chen, Y. and Shi, X. (2012b) Why does the temperature rise faster in the 121(8), 3969–3978. arid region of northwest China? Journal of Geophysical Research: Atmo- Guo, X., Wang, L. and Tian, L. (2016b) Spatio-temporal variability of vertical spheres, 117(16), 1–7. gradients of major meteorological observations around the Tibetan Plateau. Li, Z., He, Y., Wang, P., Theakstone, W.H., An, W., Wang, X., Lu, A., International Journal of Climatology, 36(4), 1901–1916. Zhang, W. and Cao, W. (2012c) Changes of daily climate extremes in 18 ULLAH ET AL.

southwestern China during 1961–2008. Global and Planetary Change, Rahimi, M. and Hejabi, S. (2017) Spatial and temporal analysis of trends in 81(1), 80, 255–272. extreme temperature indices in Iran over the period 1960–2014. International Li, X., Wang, L., Guo, X. and Chen, D. (2017) Does summer precipitation trend Journal of Climatology, 282(1), 272–282. over and around the Tibetan Plateau depend on elevation? International Rahmstorf, S., Coumou, D., Rahmstorf, S. and Coumou, D. (2016) Increase of Journal of Climatology, 37(1), 1278–1284. extreme events in a warming world. Proceedings of the National Academy of Li, J., Chen, Y.D., Gan, T.Y. and Lau, N.C. (2018) Elevated increases in human- Sciences of the United States of America, 108(44), 17905–17909. perceived temperature under climate warming. Nature Climate Change, 8(1), Ren, G. and Zhou, Y. (2014) Urbanization effect on trends of extreme tempera- 43–47. ture indices of national stations over mainland China, 1961–2008. Journal of Lu, X., Wang, L., Pan, M., Kaseke, K.F. and Li, B. (2016) A multi-scale analysis Climate, 27(6), 2340–2360. of Namibian rainfall over the recent decade—comparing TMPA satellite esti- Ren, G., Guan, Z., Shao, X. and Gong, D.Y. (2011) Changes in climatic mates and ground observations. Journal of Hydrology: Regional Studies, extremes over mainland China. Climate Research, 50(2–3), 105–111. 8(12), 59–68. Ren, Y.Y., Ren, G.Y., Sun, X.B., Shrestha, A.B., You, Q.L., Zhan, Y.J., Maenza, R.A., Agosta, E.A. and Bettolli, M.L. (2017) Climate change and pre- Rajbhandari, R., Zhang, P.F. and KM, W. (2017) Observed changes in sur- cipitation variability over the western “pampas” in Argentina. International face air temperature and precipitation in the Hindu Kush Himalayan region Journal of Climatology, 37(1), 445–463. over the last 100-plus years. Advances in Climate Change Research, 8(3), Mahlstein, I. and Knutti, R. (2010) Regional climate change patterns identified 148–156. by cluster analysis. Climate Dynamics, 35(4), 587–600. Revadekar, J.V., Hameed, S., Collins, D., Manton, M., Sheikh, M., Mahmood, R. and Babel, M.S. (2014) Future changes in extreme temperature Borgaonkar, H.P., Kothawale, D.R., Adnan, M., Ahmed, A.U., Ashraf, J., events using the statistical downscaling model (SDSM) in the trans-boundary Baidya, S., Islam, N., Jayasinghearachchi, D., Manzoor, N., Premalal, K.H. region of the Jhelum River basin. Weather and Climate Extremes, 5(1), M.S. and Shreshta, M.L. (2013) Impact of altitude and latitude on changes in 56–66. temperature extremes over South Asia during 1971–2000. International Mahmood, R., Babel, M.S. and Jia, S. (2015) Assessment of temporal and spatial Journal of Climatology, 33(1), 199–209. changes of future climate in the Jhelum River basin, Pakistan and India. Safeeq, M., Mair, A. and Fares, A. (2013) Temporal and spatial trends in air tem- Weather and Climate Extremes, 10(2), 40–55. perature on the Island of Oahu, Hawaii. International Journal of Climatol- Mann, H.B. (1945) Nonparametric tests against trend. Econometrica, 13(3), ogy, 33(13), 2816–2835. 245–259. Salman, S.A., Shahid, S., Ismail, T., Chung, E.S. and Al-Abadi, A.M. (2017) Markey, D.S. (2016) Behind China’s gambit in Pakistan. Council on Foreign Long-term trends in daily temperature extremes in Iraq. Atmospheric Relations, 12(8), 6–11. Research, 198(8), 97–107. Mastrandrea, M.D., Tebaldi, C., Snyder, C.W. and Schneider, S.H. (2011) Cur- Sayemuzzaman, M., Mekonnen, A. and Jha, M.K. (2015) Diurnal temperature rent and future impacts of extreme events in California. Climatic Change, range trend over North Carolina and the associated mechanisms. Atmo- 109(1), 43–70. spheric Research, 160(4), 99–108. Modarres, R. and De Paulo, V. (2007) Rainfall trends in arid and semi-arid Sen, P.K. (1968) Estimates of the regression coefficient based on Kendall’s tau. regions of Iran. Journal of Arid Environments, 70(2), 344–355. Journal of the American Statistical Association, 63(324), 1379–1389. Moore, D. and McCabe, G. (2003) Introduction to the Practice of Statistics. Sen, R.S., Mahmood, R., Niyogi, D., Lei, M., Foster, S.A., Hubbard, K.G., London: W. H. Freeman. Douglas, E. and Pielke, R. (2007) Impacts of the agricultural Green Mueller, V., Gray, C. and Kosec, K. (2014) Heat stress increases long-term revolution-induced land use changes on air temperatures in India. Journal of human migration in rural Pakistan. Nature Climate Change, 4(12), 182–185. Geophysical Research: Atmospheres, 112(21), 1–13. Munoz-Diaz, D. and Rodrigo, F.S. (2004) Spatio-temporal patterns of seasonal Sen, R.S., Sadegh, M., Kiany, K. and Balling, R.C. (2016) A significant rainfall in Spain (1912–2000) using cluster and principal component analy- population signal in Iranian temperature records. International Journal of sis: comparison. Annales Geophysicae, 22(5), 1435–1448. Atmospheric Sciences, 2016(1), 1–7. Nasrallah, H.A., Nieplova, E. and Ramadan, E. (2004) Warm season extreme Shahid, S., Bin, H.S. and Katimon, A. (2012) Changes in diurnal temperature temperature events in Kuwait. Journal of Arid Environments, 56(2), range in Bangladesh during the time period 1961–2008. Atmospheric 357–371. Research, 118, 260–270. Nasri, M. and Modarres, R. (2009) Dry spell trend analysis of Isfahan Province, Shahid, S., Wang, X.J., Bin, H.S., Shamsudin, S.B., Ismail, T. and Minhans, A. Iran. International Journal of Climatology, 29(10), 1430–1438. (2016) Climate variability and changes in the major cities of Bangladesh: Panda, D.K., AghaKouchak, A. and Ambast, S.K. (2017) Increasing heat waves observations, possible impacts and adaptation. Regional Environmental and warm spells in India, observed from a multiaspect framework. Journal Change, 16(2), 459–471. of Geophysical Research: Atmospheres, 122(7), 3837–3858. Sheikh, M.M., Manzoor, N., Ashraf, J., Adnan, M., Collins, D., Hameed, S., Pathak, A., Ghosh, S., Alejandro Martinez, J., Dominguez, F. and Kumar, P. Manton, M.J., Ahmed, A.U., Baidya, S.K., Borgaonkar, H.P., Islam, N., (2017a) Role of oceanic and land moisture sources and transport in the sea- Jayasinghearachchi, D., Kothawale, D.R., Premalal, K.H.M.S., Revadekar, J. sonal and interannual variability of summer monsoon in India. Journal of V. and Shrestha, M.L. (2015) Trends in extreme daily rainfall and tempera- Climate, 30(5), 1839–1859. ture indices over South Asia. International Journal of Climatology, 35(7), Pathak, A., Ghosh, S., Kumar, P. and Murtugudde, R. (2017b) Role of oceanic 1625–1637. and terrestrial atmospheric moisture sources in intraseasonal variability of Shi, T., Huang, Y., Wang, H., Shi, C.E. and Yang, Y.J. (2015) Influence of Indian summer monsoon rainfall. Scientific Reports, 7(1), 1–11. urbanization on the thermal environment of meteorological station: satellite- Pellicciotti, F., Buergi, C., Immerzeel, W.W., Konz, M. and Shrestha, A.B. observed evidence. Advances in Climate Change Research, 6(1), 7–15. (2012) Challenges and uncertainties in hydrological modeling of remote Shi, J., Cui, L., Ma, Y., Du, H. and Wen, K. (2018) Trends in temperature Hindu Kush–Karakoram–Himalayan (HKH) basins: suggestions for calibra- extremes and their association with circulation patterns in China during tion strategies. Mountain Research and Development, 32(1), 39–50. 1961–2015. Atmospheric Research, 212(2), 259–272. Priya, P., Mujumdar, M., Sabin, T.P., Terray, P. and Krishnan, R. (2015) Impacts Sillmann, J., Kharin, V.V., Zwiers, F.W., Zhang, X. and Bronaugh, D. (2013) of Indo-Pacific sea surface temperature anomalies on the summer monsoon Climate extremes indices in the CMIP5 multimodel ensemble: part 2. Future circulation and heavy precipitation over northwest India–Pakistan region dur- climate projections. Journal of Geophysical Research: Atmospheres, 118(1), ing 2010. Journal of Climate, 28(9), 3714–3730. 2473–2493. Priya, P., Krishnan, R., Mujumdar, M. and Houze, R.A. (2017) Changing mon- Sonali, P. and Kumar, N.D. (2013) Review of trend detection methods and their soon and midlatitude circulation interactions over the western Himalayas and application to detect temperature changes in India. Journal of Hydrology, possible links to occurrences of extreme precipitation. Climate Dynamics, 476(7), 212–227. 49(7–8), 2351–2364. Subash, N. and Sikka, A.K. (2014) Trend analysis of rainfall and temperature Ragettli, S., Immerzeel, W.W. and Pellicciotti, F. (2016) Contrasting climate and its relationship over India. Theoretical and Applied Climatology, change impact on river flows from high-altitude catchments in the Himala- 117(3–4), 449–462. yan and Andes Mountains. Proceedings of the National Academy of Sciences Sun, Q., Miao, C., Duan, Q. and Wang, Y. (2015) Temperature and precipi- of the United States of America, 113(33), 9222–9227. tation changes over the Loess Plateau between 1961 and 2011, based on ULLAH ET AL. 19

high-density gauge observations. Global and Planetary Change, Yan, L. and Liu, X. (2014) Has climatic warming over the Tibetan Plateau 132(1), 1–10. paused or continued in recent years. Journal of Earth, Ocean and Atmo- Sun, W., Mu, X., Song, X., Wu, D., Cheng, A. and Qiu, B. (2016) Changes in spheric Sciences, 1(1), 13–28. extreme temperature and precipitation events in the Loess Plateau (China) dur- Yan, G., Qi, F., Wei, L., Aigang, L., Yu, W., Jing, Y., Aifang, C., Yamin, W., ing 1960–2013 under global warming. Atmospheric Research,168(9),33–48. Yubo, S., Li, L. and Qianqian, M. (2015) Changes of daily climate extremes Sun, X.B., Ren, G.Y., Shrestha, A.B., Ren, Y.Y., You, Q.L., Zhan, Y.J., Xu, Y. in Loess Plateau during 1960–2013. Quaternary International, 371(6), 5–21. and Rajbhandari, R. (2017) Changes in extreme temperature events over the Yao, J. and Chen, Y. (2015) Trend analysis of temperature and precipitation in Hindu Kush Himalaya during 1961–2015. Advances in Climate Change the Syr Darya Basin in central Asia. Theoretical and Applied Climatology, Research, 8(3), 157–165. 120(3–4), 521–531. Tao, H., Fraedrich, K., Menz, C. and Zhai, J. (2014) Trends in extreme tempera- You, Q., Kang, S., Pepin, N. and Yan, Y. (2008) Relationship between trends in ture indices in the Poyang Lake Basin, China. Stochastic Environmental temperature extremes and elevation in the eastern and central Tibetan Pla- Research and Risk Assessment, 28(6), 1543–1553. teau, 1961–2005. Geophysical Research Letters, 35(4), 1–7. Thom, H.C.S. (1966) Some Methods of Climatological Analysis. Geneva: World You, Q., Kang, S., Pepin, N., Flügel, W.A., Yan, Y., Behrawan, H. and Huang, J. Meteorologcal Organization (WMO). (2010) Relationship between temperature trend magnitude, elevation and Thomas, J. and Prasannakumar, V. (2016) Temporal analysis of rainfall mean temperature in the Tibetan Plateau from homogenized surface stations (1871–2012) and drought characteristics over a tropical monsoon-dominated and reanalysis data. Global and Planetary Change,71(1–2), 124–133. state (Kerala) of India. Journal of Hydrology, 534(3), 266–280. You, Q., Kang, S., Aguilar, E., Pepin, N., Flugel, W.A., Yan, Y., Xu, Y., Trenberth, E.K., Fasullo, T.J. and Sheperd, G.T. (2015) Attribution of climate Zhang, Y. and Huang, J. (2011) Changes in daily climate extremes in China extreme events. Nature Climate Change, 5(1), 725–730. and their connection to the large-scale atmospheric circulation during Ullah, S., You, Q., Ullah, W. and Ali, A. (2018) Observed changes in precipita- 1961–2003. Climate Dynamics, 36(11–12), 2399–2417. tion in China–Pakistan Economic Corridor during 1980–2016. Atmospheric You, Q., Ren, G.Y., Zhang, Y.Q., Ren, Y.Y., Sun, X.B., Zhan, Y.J., Shrestha, A. Research, 210(4), 1–14. B. and Krishnan, R. (2017) An overview of studies of observed climate Ullah, S., You, Q., Ali, A., Ullah, W., Jan, M.A., Zhang, Y., Xie, W. and Xie, change in the Hindu Kush Himalayan (HKH) region. Advances in Climate X. (2019) Observed changes in maximum and minimum temperatures over Change Research, 8(3), 141–147. China-Pakistan economic corridor during. Atmospheric Research, 216(2), Zahid, M. and Rasul, G. (2011) Frequency of extreme temperature and precipita- 37–51. tion events in Pakistan 1965–2009. Science International, 23(4), 313–319. ur Rahman, A. and Khan, A.N. (2011) Analysis of flood causes and associated Zahid, M. and Rasul, G. (2012) Changing trends of thermal extremes in socio-economic damages in the Hindukush region. Natural Hazards, 59(3), Pakistan. Climatic Change, 113(4), 883–896. 1239–1260. Zahid, M., Richard, B., Valerio, L. and Bramati, M.C. (2017) Return levels of Wang, S.Y., Davies, R.E., Huang, W.R. and Gillies, R.R. (2011) Pakistan’s two- temperature extremes in southern Pakistan. Earth System Dynamics, 8(4), stage monsoon and links with the recent climate change. Journal of Geo- 1263–1278. physical Research: Atmospheres, 116, D16114. Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Wang, B., Liu, J., Kim, H.J., Webster, P.J. and Yim, S.Y. (2012) Recent change of the Trewin, B. and Zwiers, F.W. (2011) Indices for monitoring changes in global monsoon precipitation (1979–2008). Climate Dynamics, 39(5), 1123–1135. extremes based on daily temperature and precipitation data. WIREs: Climate Wang, P.X., Wang, B., Cheng, H., Fasullo, J., Guo, Z.T., Kiefer, T. and Liu, Z. Change, 2(6), 851–870. Y. (2017) The global monsoon across time scales: mechanisms and outstand- ing issues. Earth-Science Reviews, 174(11), 84–121. van Wijngaarden, W.A. (2015) Temperature trends in the Canadian arctic during – – – 1895 2014. Theoretical and Applied Climatology, 120(3 4), 609 615. How to cite this article: Ullah S, You Q, Ullah W, Xu, M., Kang, S., Wu, H. and Yuan, X. (2018) Detection of spatio-temporal var- iability of air temperature and precipitation based on long-term meteorologi- Ali A, Xie W, Xie X. Observed changes in tempera- cal station observations over Tianshan Mountains, Central Asia. Atmospheric ture extremes over China–Pakistan Economic Corri- Research, 203(12), 141–163. dor during 1980–2016. Int J Climatol. 2018;1–19. Yamada, T.J., Takeuchi, D., Farukh, M.A. and Kitano, Y. (2016) Climatological characteristics of heavy rainfall in northern Pakistan and atmospheric block- https://doi.org/10.1002/joc.5894 ing over western Russia. Journal of Climate, 29(21), 7743–7754.