Analysis of Sub-Daily Precipitation for the Pannex Region

atmosphere

Article Analysis of Sub-Daily Precipitation for the PannEx Region

Monika Lakatos 1,*, Olivér Szentes 1, Ksenija Cindri´cKalin 2, Irena Nimac 2, Katja Kozjek 3, Sorin Cheval 4 , 4 4 5,6 5 7 7 Alexandru Dumitrescu , Adrian Iras, oc , Petr Stepanek , Aleš Farda , Peter Kajaba , Katarína Mikulová , Dragan Mihic 8, Predrag Petrovic 8, Barbara Chimani 9 and David Pritchard 10

1 Hungarian Meteorological Service, 1024 Budapest, Hungary; [email protected] 2 Croatian Meteorological and Hydrological Service, 10000 Zagreb, Croatia; [email protected] (K.C.K.); [email protected] (I.N.) 3 Slovenian Environment Agency, 1000 Ljubljana, Slovenia; [email protected] 4 National Meteorological Administration, 013686 Bucharest, Romania; [email protected] (S.C.); [email protected] (A.D.); [email protected] (A.I.) 5 Global Change Research Institute, Czech Academy of Sciences, 603 00 Brno, Czech Republic; [email protected] (P.S.); [email protected] (A.F.) 6 Czech Hydrometeorological Institute, 616 67 Brno, Czech Republic 7 Slovak Hydrometeorological Institute, 833 15 Bratislava, Slovakia; [email protected] (P.K.); [email protected] (K.M.) 8 Republic Hydrometeorological Service of Serbia, 11030 Beograd, Serbia; [email protected] (D.M.); [email protected] (P.P.) 9 Zentralanstalt für Meteorologie und Geodynamik, 1190 Wien, Austria; [email protected] 10 School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK; [email protected] * Correspondence: [email protected]; Tel.: +36-1-346-4725

Citation: Lakatos, M.; Szentes, O.; Abstract: The PannEx is a GEWEX-initiated, community driven research network in the Pannonian Cindri´cKalin, K.; Nimac, I.; Kozjek, Basin. One of the main scientiﬁc issues to address in PannEx is the investigation of precipitation K.; Cheval, S.; Dumitrescu, A.; Irasoc, , extremes. Meteorological Services in the PannEx area collected the hourly precipitation data and A.; Stepanek, P.; Farda, A.; et al. commonly used a computer program, which was developed in the INTENSE project, to produce Analysis of Sub-Daily Precipitation a set of global hydro-climatic indices. Calculations are carried out on data aggregated 1-, 3- and for the PannEx Region. Atmosphere 2021, 12, 838. https://doi.org/ 6-h intervals. Selected indices are analyzed in this paper to assess the general climatology of the 10.3390/atmos12070838 short-term precipitation in the Pannonian basin. The following indices are illustrated on maps and graphs: the annual mean and maxima of 1-h, 3-h and 6-h sums, the count of 3-hr periods greater Academic Editor: than 20 mm thresholds, the maximum length of wet hours, the timing of wettest hour and the 1-h Adina-Eliza Croitoru precipitation intensity. The seasonal trends of the 1-h precipitation intensity were tested from 1998 to 2019. Analysis of sub-daily precipitation has been limited by the availability of data on a global Received: 20 May 2021 or a regional scale. The international effort made in this work through collaboration in the PannEx Accepted: 21 June 2021 initiative contributes to enlarging the data availability for regional and global analysis of sub-daily Published: 29 June 2021 precipitation extremes.

Publisher’s Note: MDPI stays neutral Keywords: sub-daily precipitation; sub-daily precipitation indices; PannEx RHP; Pannonian Basin with regard to jurisdictional claims in Experiment; climate of the carpathian region; INTENSE project published maps and institutional afﬁl- iations.

1. Introduction Heavy rainfall and storm events constantly trigger important damages and signiﬁcant Copyright: © 2021 by the authors. casualties in many regions, and enhanced understanding the variability of precipitation Licensee MDPI, Basel, Switzerland. This article is an open access article extremes would enable our communities to cope more efﬁciently with the challenges distributed under the terms and associated with the warming climate [1]. Understanding how and why precipitation conditions of the Creative Commons extremes vary is vital for disaster preparedness and to enable adequate water resource Attribution (CC BY) license (https:// management. This requires both understanding of how extremes have varied in the past creativecommons.org/licenses/by/ and the ability to provide estimates for how they might change under warming climate. 4.0/). One of the key statements of the IPCC Fifth Assessment Report centres around changes

Atmosphere 2021, 12, 838. https://doi.org/10.3390/atmos12070838 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 838 2 of 18

in the extremes of observed precipitation, e.g., ‘There are likely more land regions where the number of heavy precipitation events has increased than where it has decreased’ [2]. Analyses of the station data that have contributed to available global datasets display increasing trends in annual maximum daily precipitation [3]. The observed more frequent heavy precipitation events are also consistent with increasing amounts of water vapor in the atmosphere due to global warming [4]. In recent decades, an increasing number of studies have reported the presence of significant positive trends in precipitation extremes in Europe e.g., [5–9]. Significant trends in precipitation extremes over Europe have been observed since the middle of the 20th century in regional studies for Northern Europe [10], the UK [11], the Mediterranean region [12], and western and eastern parts of the Czech Republic [13] and Poland [14]. [15] showed that despite the considerable decadal variability, 5-, 10-, and 20-year events of 1-day and 5-day precipitation amounts for the first 20 years in the period 1951–2010 became more common in the analyzed 60 years for the daily precipitation series from the European Climate Assessment and Dataset (ECA&D, http://www.ecad.eu (accessed on 5 May 2021)) project [16,17]. The area in focus of this study is the Pannonian basin and its surroundings. The Pan- nonian/Carpathian basin (both names are used) is part of the larger Carpathian region. The precipitation climatology for the Carpathian region together with other climate variables are discussed either at seasonal or monthly scale [18,19] ( based on the CARPATCLIM dataset. The CARPATCLIM data covers the Carpathian Region and provide a homogenized [20,21], harmonized and gridded [22] dataset for climate studies. The precipitation amounts showed no significant trend, though it increased slightly on an annual basis in the period 1961–2010. The changes in the number of very wet days (daily precipitation > 20 mm) range between -2 and +3 days in extended regions. The highest increase appears in the Northeast Carpathians and the Bihor Mountains i.e., 7 days [23]. Considering the precipitation changes in Hungary, in the centre of the Carpathian region, a decrease of the annual precipitation sum is not remarkable (3%, 1901–2019). From the beginning of the 1990s, precipitation has been increasing both on annual and seasonal scales; however, this rise is not significant. Recent years have been dominated by the extremes. The magnitude of the change in precipitation intensity (mm/day) is about 1.3 mm/day in the countrywide average. The number of days with a daily sum above 20 mm increased by 1 day in the period 1901–2017 [24]. In Croatia, the observed changes in annual and seasonal precipitation amounts are also generally weak, particularly when trends from the beginning of 20th century were considered [25]. However, in the eastern mainland (Pannonia plain) a significant positive trend in the autumn season was detected from the second half of the last century [26]. It was associated with more very wet days and with an increase of their contribution to the total precipitation as well as an increase in maximal 1- and 5-day precipitation and in daily precipitation intensity. In general, the above-mentioned studies relied on daily or longer durations of precipitation, whereas studies documenting the characteristics of sub-daily precipitation are fewer. However, such events are of great importance for society and economy since extreme sub-daily precipitation may be associated with hazardous events such as floods, soil erosion, and debris. Moreover, mountains can play an important role by blocking further movement of cloud systems or even enhancing precipitation-related processes leading to possible flash flood events [27]. Due to a higher number of impervious surfaces, urban areas have a higher risk of pluvial flooding. Hence, cities in the vicinity of mountains can be additionally endangered since their drainage systems need to handle additional water amounts. Fowler et al. [28] detected a notable minority of studies dealing with short-term rainfall, both on global or regional scales. Such studies are also sparse in the Pannonian region. Frequent occurrence, together with broad publicity for heavy rainfall events in recent years, has stimulated debates on the effects of climate change on the frequency and the magnitude of floods [29]. Due to spatial and temporal limitations of conventional climate models for Atmosphere 2021, 12, 838 3 of 18

resolving convective processes [30], measurements of short-term precipitation are of great importance for the analysis of such events. For Hungary, Lakatos et al. [31] analyzed the return values of the 60-min extreme rainfall events using the measurements of the automatic weather station network. The return period of extremely high values of short-term rainfall has shortened in recent years in Hungary, as it is shown in a case study for the Pécs-Pogány meteorological station [32]. In Croatia, the analyses of a short-term precipitation were made for a few selected locations, mainly in urban areas [27,33,34] and over the region of Istria [35]. Extremes of sub-daily precipitation have been studied in the Czech Republic by Beranova et al. 2018. They compared the characteristics of observed sub-daily precipitation extremes with those simulated by several regional climate models driven by reanalyses and examined diurnal cycles of hourly precipitation and their dependence on intensity and surface temperature. Beranová et al. [36] showed that convection is not captured realistically. Hanel et al. [29] analysed sub-daily summer precipitation characteristics for 17 stations in the Czech Republic in the period 1961–2011 and found that the number of stations with significant positive trends is considerably larger than the number of stations with significant negative trends. Moreover, a significant trend in rain event characteristics is found only for a small number of stations, with the exception of the rain rate, which shows a systematic increase, and the consistent decrease in event duration. According to the Clausius-Clapeyron relation, water vapor pressure in the atmosphere increases at a rate of 7% per Celsius degree (for constant relative humidity). Accordingly, it can be expected that the sub-daily precipitation will increase at the same rate. However, there is already evidence of the scaling of sub-daily precipitation with temperature that exceeds the expected 7% per degree e.g., [37,38]; thus, an increase in short-duration extremes as a consequence of the warming atmosphere prompts concern. In particular, on small temporal and spatial scales, rates of extreme precipitation are influenced by atmospheric circulation and vertical stability in addition to local moisture availability [39]. However, analysis of sub-daily precipitation has been limited by the availability of data on a global scale [5]. The situation is worse for sub-daily data, although efforts have been made re- cently to construct the first Global Sub-Daily Rainfall (GSDR) dataset, comprising over 23,000 hourly gauges [40], though with relatively short records in many locations. The first major international effort to focus on global sub-daily rainfall extremes is the INTENSE (INTElligent use of climate models for adaptatioN to non-Stationary hydrological Extremes) project. The INTENSE project is using a novel and fully-integrated data-modelling approach to enhance our understanding of the nature and drivers of global precipitation extremes and change on societally relevant timescales, leading to improved high-resolution climate model representation of extreme rainfall processes [41]. The INTENSE project is in conjunction with the World Climate Research Programme (WCRP)’s Grand Challenge on ‘Understanding and Predicting Weather and Climate Extremes’ and the Global Water and Energy Exchanges Project (GEWEX) Science questions. The first step towards achieving the project goals is to construct a new global sub-daily precipitation dataset. Comprehensive, open source quality control software is being developed to set a new standard for verifying sub-daily precipitation data and a set of global hydro-climatic indices to be produced. The derived indices, e.g., monthly/annual maxima, will be freely available to the wider scientific community. The PannEx (Pannonian Basin Experiment) is also a GEWEX-related initiative. It is a Regional Hydroclimate Project (RHP) of the GEWEX GHP (Global Hydrology Panel). It is a GEWEX-initiated, community driven research network in the Pannonian/Carpathian Basin. It aims to achieve a better understanding of the Earth System components and their inter- actions in the Pannonian Basin (PannEx–https://sites.google.com/site/projectpannex/ (accessed on 5 May 2020)). The international efforts in PannEx involve research institu- tions, universities and national meteorological and hydrological services in an integrated approach towards identifying and increasing the capacity to face climate change in the Pannonian Basin. The PannEx research community has prepared a white book with the Atmosphere 2021, 12, x FOR PEER REVIEW 4 of 19

Atmosphere 2021, 12, 838 towards identifying and increasing the capacity to face climate change in the Pannonian4 of 18 Basin. The PannEx research community has prepared a white book with the objective of identifying the main scientific issues to address, with the analysis of the precipitation extremesobjective amongst of identifying them the[41,42]. main Thus, scientiﬁc the issuesPannEx to community address, with is providing the analysis an of excellent the pre- frameworkcipitation extremes for the GEWEX amongst RHP them and [42, 43GHP]. Thus, joint thework. PannEx The communitypurpose of this is providing paper is anto contributeexcellent framework to INTENSE for theinitiative GEWEX with RHP an and international GHP joint work.effort Thethrough purpose collaboration of this paper of Meteorologicalis to contribute toServices INTENSE in the initiative Pannonian/Carpathian with an international basin effort by collecting through collaboration and analysing of theMeteorological hourly data Servicesand providing in the Pannonian/Carpathianinitial analyses of the sub-daily basin by precipitation collecting and climatology analysing inthe the hourly PannEx data region. and providing The commonly initial analyses used software of the sub-daily Indices for precipitation quality control climatology and der- in ivationthe PannEx of several region. sub-daily The commonly precipitation used software indices Indiceswas implemented for quality controlin the INTENSE and derivation pro- jectof several at Newcastle sub-daily University. precipitation We start indices by intr wasoducing implemented the area in and the the INTENSE data used project in this at study.Newcastle We University.introduce the We computer start by introducing program we the used. area and Then, the we data continue used in thisby analyzing study. We andintroduce discussing the computer the averages program and wetrends used. of Then,some weselected continue indices by analyzing with a focus and discussingon the cli- matologythe averages of the and sub-daily trends of precipitation. some selected The indices last section with a focusis devoted on the to climatology the main conclu- of the sions.sub-daily precipitation. The last section is devoted to the main conclusions.

2.2. Area Area in in Focus Focus TheThe Pannonian BasinBasin (Figure(Figure1 )1) is is enclosed enclosed by by the the Carpathians Carpathians and and the the Transylvanian Transylva- nianPlateau Plateau to the to east the and east north and north [44,45 ].[43,44]. This article This article focuses focuses on the areaon the of area the countries of the countries located locatedin the domain in the domain of PannEx of butPannEx only but partially only partially covers the covers domain: the Czechdomain: Republic Czech (CZ)Republic and (CZ)Slovakia and (SVK)Slovakia to the(SVK) north, to the Austria north, (AT), Austria Hungary (AT), (HU) Hungary andRomania (HU) and (RO) Romania in the (RO) middle in theand middle Slovenia and (SLO), Slovenia Croatia (SLO), (CRO) Croatia and Serbia(CRO) (SRB)and Serbia to the (SRB) south. to the south.

Figure 1. The Pannonian Basin with major orographic and river systems.systems. The abbreviation of the names of the targeted countries (left) and the climatic sub regions with the selected stations for the analysis (right). countries (left) and the climatic sub regions with the selected stations for the analysis (right).

TheThe Carpathians Carpathians are the longest mountain range and the geographic barrier between CentralCentral Europe,Europe, EasternEastern Europe Europe and and the the Balkans. Balkans. Carpathian/Pannonian Carpathian/Pannonian Basin Basin is bordered is bor- deredby the by Alps the in Alps the northwest,in the northwest, the Dinaric the Dina Alpsric in Alps thesouthwest in the southwest and by theand Carpathiansby the Car- pathiansin northeastern in northeastern direction, direction, and divided and by di thevided Danube by the River. Danube The CarpathiansRiver. The Carpathians constitute a constitutegeographic a barriergeographic between barrier the between cold and the dry cold continental and dry climatecontinental of Eastern climate Europe, of Eastern the Europe,temperate the climate temperate of Central climate Europe of Central and theEurope warm and and the wet warm Mediterranean and wet Mediterranean climate of the climateBalkans of [18 the]. Precipitation Balkans [17]. is Precipitation a climate parameter is a climate with highparameter temporal with and high spatial temporal variability and spatialin the region. variability Its spatial in the distributionregion. Its spatial is determined distribution by distance is determined from the by seas, distance continentality from the seas,and topography,continentality as welland topography, as passing weather as well systems. as passing Large-scale weather drivers systems. such Large-scale as North- driversAtlantic such Oscillation as North-Atlantic (NAO) can Oscillation influence the (NAO) tracks can of theseinfluence systems the tracks [46]. However, of these sys- the temsCarpathian [45]. However, chain is very the longCarpathian (more thanchain 1500 is very km, long but only (more the than Tatra 1500 Mountains km, but are only above the Tatra2000 m)Mountains and it has are a curvedabove 2000 shape; m) consequentlyand it has a curved the Carpathians shape; consequently are not always the Carpa- able to thiansstop the are humid not always oceanic able air to masses stop the coming humi fromd oceanic north-west. air masses Topography coming from also north-west. plays a role Topographyin the modification also plays of the a role air flowsin the inmodification the large central of the plain,air flows the in Carpathian the large central basin [24 plain,]. In thewinter, Carpathian precipitation basin occurs [23]. In because winter, of precipitation large-scale, mainly occurs stratiform because of precipitation large-scale, systems, mainly whereas convective precipitation dominates in late spring, summer and early autumn and develops many times locally, in unstable air masses. In the Mediterranean climate, most of

Atmosphere 2021, 12, 838 5 of 18

the precipitation is obtained in autumn or winter, which is typically convective due to the unstable air masses above the sea. The climate features of the sub-regions are described in Kocsis [47]. In the following, we outlined the foremost determinant features of the precipitating climate of the sub regions I. to IX.The Pannonian Great Plain (I): The lowland situated in the inner part of the Basin with scarce precipitation. In the middle of the Great Plain the flow is more moderate, typically north-westerly, while in the southern part south wind blows; Drava- Sava region (II): its climate is determined by Mediterranean character and by the varied topography, such as the precipitation-increasing effect of the mountains; Transdanubian hills with the Burgenland and the mediMurje (III): with favourable precipitation, growing Mediterranean character in the south; Little Plain (VI): the oceanic effect is stronger, there is more precipitation compared to the Great Plain and this is one of the windiest areas of the Pannonian basin; Vienna basin together with the upper Moravian hills (V): right up to the mountain frame, more affected by the ocean; Upper-Danube region with the medium and high mountains (VI): variation of deep valleys and closed basins, the local climate reflects the variation of the topography; Southern and Eastern Carpathians (VII) and Transylvanian Basin (VIII) and the Bihor-mountains (IX): the climate has more continental character there, which appears most intensively at higher elevation in the closed basins. The precipitation of cyclones from the west and from the Mediterranean region fall primarily due to the elevating effect of the topography. The flow here-due to the basin effect-comes as eddy-wind from all directions.

3. Data Before automation, pluviographs were used to measure the precipitation at National Meteorological and Hydrological Services in the PannEx region. The pluviographs were also suitable for registering even at minute intensity; however, the evaluation of the registering paper was different from automatic sampling. In Hungary, for example, the most extreme partial sums were entered into the database, instead of the 10-min sums, which were stored from the late 90s onwards. Since the most intense 60-min sums are slightly different from the 1-h sums, to keep the homogeneity of the data sampling, the Hungarian data series from 30 stations used in this study only covers the period from 1998 to 2019, typically a period of automatic measurements. The Croatian mainland, as a part of the PannEx region, was covered here by 10 meteorological stations from the regular network of Croatian Meteorological and Hydrological Service (DHMZ). Short−term precipitation registers (with a discretization of 5 min) from the classical Hellman pluviograph were digitized, and hourly data from a common 1981−2019 period was selected for this study. Preliminary quality control compared the daily precipitation amounts registered by the ombrograph and a rain gauge at the same location. Initial data used rainfall measurements from 29 Slovenian pluviographic stations with at least 15 years of data, measured in 5-min time intervals. Although incoming station measurements undergo regular quality control, not all errors in the data can be ﬁltered out. Additional ﬁlters at different stages of the process were therefore applied. Homogenized data sets of daily rainfall from rain gauges were used as a reference data in correcting the initial data. However, only two of the Slovenian stations are situated in the PannEx area in focus, and all derived Slovenian sub- daily precipitation indices were offered to integrate to the global sub-daily precipitation dataset. In Romania, hourly data were extracted from paper pluviogram charts until 2018, and after automatic measurements using the tipping bucket and weighing precipitation gauges started in 2008. The pluviogram charts are available only for the warm season, namely from April to October. For the Czech Republic, heated rain gauges with tipping buckets were used for automated precipitation measurements. In the last few years, the professional station network introduced weighing rain gauges that replaced the original rain gauges. The period of these measurements covers about the last 20 years. For Slovakia, the situation is very similar, but weighing rain gauges started to replace gauges with tipping Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 19

Atmosphere 2021, 12, 838 urements covers about the last 20 years. For Slovakia, the situation is very similar,6 ofbut 18 weighing rain gauges started to replace gauges with tipping buckets earlier, since 2004, and from 2015 they became the only automated instrument in the station network. Although the data sharing agreements between WMO nations have moved in the buckets earlier, since 2004, and from 2015 they became the only automated instrument in direction of further sharing, the data was not collected to a common database. The par- the station network. ticipating researchers run the software to derive the sub-daily precipitation indices Although the data sharing agreements between WMO nations have moved in the themselves. While there are obstacles to the exchange of long-term high temporal reso- direction of further sharing, the data was not collected to a common database. The partici- lution precipitation data, there have been fewer barriers to the exchange of the derived pating researchers run the software to derive the sub-daily precipitation indices themselves. indices. The strong restrictions on sharing daily and sub-daily data in some countries While there are obstacles to the exchange of long-term high temporal resolution precip- (despite a WMO resolution to the contrary—WMO 2016) typically do not apply to shar- itation data, there have been fewer barriers to the exchange of the derived indices. The ing indices. As a result of this joint effort of the Meteorological Services in the PannEx strong restrictions on sharing daily and sub-daily data in some countries (despite a WMO region, all the derived indices were offered to enlarge the global sub-daily rainfall da- resolution to the contrary—WMO 2016) typically do not apply to sharing indices. As a taset.result of this joint effort of the Meteorological Services in the PannEx region, all the derived indicesThe were input offered of the tocommonly enlarge the used global software sub-daily was rainfalleither the dataset. hourly or the 10-min data. The longestThe input records of the go commonly back to 1950 used and software earlier was for eithersome Romanian the hourly stations or the 10-min (Figure data. 2). TheThe series longest from records the gotwo back Slovenian to 1950 stations and earlier started for some in 1970 Romanian and 1975. stations The Croatian (Figure2 ).series The areseries longer from than the two35 years Slovenian starting stations from 1981. started Overall in 1970 the and data 1975. density The is Croatian the highest series from are thelonger 1990s. than The 35 series years for starting two stations from 1981. are availa Overallble thefor dataSerbia density from early is the 1990s, highest but from most the of the1990s. data The used series for the for analysis two stations begin are in 1998, available as for for Austria, Serbia fromCzech early Republic, 1990s, Slovakia but most and of Hungary.the data used However, for the many analysis stations begin only in 1998, have as re forlatively Austria, short Czech records, Republic, with a Slovakia lot of miss- and ingHungary. data. The However, data coverage many stations in time only and have the relatively percent shortof missing records, data with is a varying lot of missing from countrydata. The to data country. coverage Beforehand, in time and we theunderline percent that of missing results datafor stations is varying only from located country in the to focuscountry. area Beforehand, are shown here. we underline We included that resultsto the analysis for stations gauges only with located less inthan the 50 focus percent area ofare missing shown values. here. We The included data availability to the analysis by country gauges for with the lesslongest than records 50 percent can ofbe missingseen in Figurevalues. 2. The data availability by country for the longest records can be seen in Figure2.

50 45 AT (8) 40 CRO (10) 35 CZ (27) 30 RO (38) 25 20 SI (2)

PercentMissing [%] PercentMissing 15 SK (17) 10 SR (2) 5 0 HU (30) 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 Start_year

Figure 2. Data availability for this study by country. Figure 2. Data availability for this study by country. 4. Methods 4.4.1. Methods The Commonly Used Software: INDICES 4.1. TheA teamCommonly at Newcastle Used Software: University INDICES is working on a global dataset of observed sub-daily precipitation indices associated with the INTENSE (INTElligent use of climate models A team at Newcastle University is working on a global dataset of observed sub-daily for adaptatioN to non-Stationary hydrological Extremes) project. They have developed a precipitation indices associated with the INTENSE (INTElligent use of climate models for Python library, INDICES, for calculating indices for sub-daily precipitation data. INDICES adaptatioN to non-Stationary hydrological Extremes) project. They have developed a performs quality control of the data before deriving the sub-daily indices [48]. It also Python library, INDICES, for calculating indices for sub-daily precipitation data. INDI- provides the summary statistics of the indices. All contributors to this article relied on CESthis library,performs with quality the assistance control of of the the data INTENSE before deriving team. The the indices sub-daily themselves indices are[47]. adapted It also and extended versions of the ETCCDI indices. These indices were identiﬁed following discussions with the climate observations and modelling communities [49] and correspond, where possible, to the existing daily ETCCDI indices [50,51]. These indices condense Atmosphere 2021, 12, 838 7 of 18

information from the distribution of daily or sub-daily data to provide measures of intensity, frequency, and duration usually on monthly, seasonal, or annual timescales. The team is planning to release the station-level indices when possible (according to licence restrictions), and they are also producing and publishing a gridded version of the indices dataset with as much global coverage as possible. Calculations are generally carried out on data aggregated in 1-, 3- and 6-h intervals, although some indices are only calculated using 1-h data. Each index is calculated on both a monthly and annual basis. Most indices are computed as time series, thus allowing us to calculate the trends. The calculation details for the relevant indices are given in Table1.

Table 1. The list of indices used in this study.

Indices Group Abbreviation Description Illustrated on Maps/Graphs annual mean and the Maxima Rx1hr Simple maxima of 1-h sum maximum (maps) annual mean and the Maxima Rx3hr Simple maxima of 3-h sum maximum (maps) annual mean and the Maxima Rx6hr Simple maxima of 6-h sum maximum (maps) Count of 3-h periods greater than a Frequency/Threshold R3hr20 mm annual count (graphs) 20 mm threshold Maximum length of wet spell. (i.e., consecutive Duration MxLWS annual (graphs) wet hours). Wet hours are deﬁned as ≥0.1 mm) Timing of wettest hour of each wet day. Calculated on a month-wise basis as the mode of the timing Diurnal Cycle MoWH monthly (graphs) (hour of day) of the wettest hour on each wet day, where wet days are deﬁned as ≥1 mm seasonal mean and change General SPII1hr Mean precipitation in wet hours (maps)

4.2. The Selected Indices and Applied Statistics We have chosen several key indices (Table1) to visualize on maps and analyse for the entire region. For a few selected meteorological stations some of the indices are presented on graphs. The seasonal trends for SPII1hr indices were tested with the Mann-Kendall test at the 0.05 signiﬁcance level and the Sen-slope was computed [52]. The decadal change and its signiﬁcance is illustrated on maps. We used R statistical language and environment for trend analysis and for mapping [53].

5. Results and Discussion 5.1. Spatial and Temporal Pattern of Sub-Daily Precipitation Indices After processing the code of the INDICES software numerous results are available for 1-h, 3-h and 6-h sub-totals to analyse. Here, an initial analysis of the sub-daily precipitation climatology of the larger Carpathian region (Figure 1.) with the main focus for the Pannonian basin are discussed. The annual average of 1-h maxima (Rx1hr) and the maximum 1-h precipitation in the recording period are presented in Figure 3. The size of the markers represents the lengths of the available data series, whereas different colours present the range of indices’ values. Large spatial variability can be seen on both the mean and the maximum of Rx1hr. The highest mean values, 24–26 mm, characterize the central part of the region for the most recent period (Figure 3, left, smallest dots). Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 19 This could be due to the openness of the northwestern part of the domain to prevailing weather systems coming from the Atlantic. Additionally, flat continental terrain warms up more compared to surrounding mountainous and coastal areas which may 5.additionally Results and enhance Discussion convection processes. Similar or slightly smaller values appear for 5.1.the Spatialmid-long and dataTemporal (31–50 Pattern year) of series Sub-Daily in Slovenia, Precipitation Croatia Indices and Romania. The datasets longer than 50 years (largest dots) indicate lower mean 1-h maxima in Transylvania, than After processing the code of the INDICES software numerous results are available outside the southern Carpathians. The highest annual average of 1-h precipitation ex- for 1-h, 3-h and 6-h sub-totals to analyse. Here, an initial analysis of the sub-daily pre- ceeds 26 mm there. No clear spatial pattern or differences between the periods examined cipitation climatology of the larger Carpathian region (Figure 1.) with the main focus for can be observed in the map of maxima of the 1-h totals (Figure 3, right). Large values, the Pannonian basin are discussed. above 60 mm occur in all three examined periods, less in Czechia, but mainly in the The annual average of 1-h maxima (Rx1hr) and the maximum 1-h precipitation in middle of the region. Regarding Rx3hr and Rx6hr, the average annual maxima mainly the recording period are presented in Figure 3. The size of the markers represents the Atmosphere 2021, 12, 838 occur in the central part of the region (Figures 4 and 5 left, HU and RO) while the abso- 8 of 18 lengths of the available data series, whereas different colours present the range of indices’ lute maxima exceeding 85 mm in 3 h and 100 mm in 6 h occur at the western and north- values. Large spatial variability can be seen on both the mean and the maximum of ern part of the region (Figures 4 and 5 right). Rx1hr. The highest mean values, 24–26 mm, characterize the central part of the region for the most recent period (Figure 3, left, smallest dots). This could be due to the openness of the northwestern part of the domain to prevailing weather systems coming from the Atlantic. Additionally, flat continental terrain warms up more compared to surrounding mountainous and coastal areas which may additionally enhance convection processes. Similar or slightly smaller values appear for the mid-long data (31–50 year) series in Slovenia, Croatia and Romania. The datasets longer than 50 years (largest dots) indicate lower mean 1-h maxima in Transylvania, than outside the southern Carpathians. The highest annual average of 1-h precipitation exceeds 26 mm there. No clear spatial pattern or differences between the periods examined can be observed in the map of maxima of the 1-h totals (Figure 3, right). Large values, above 60 mm occur in all three examined periods, less in Czechia, but mainly in the middle of the region. Regarding Rx3hr and Rx6hr, the average annual maxima mainly FigureFigure 3. Annual 3. Annual average average of 1-h ofoccur 1-h maxima maxima in the (Rx1hr) (Rx1hr)central (left ( leftpart)) and andof the the maximumregionmaximum (Figures 1-h 1-h precipitation precipitation 4 and 5 left, (right (rightHU). The and). Thesize RO) sizeof whilethe of markers the the markers absolute maxima exceeding 85 mm in 3 h and 100 mm in 6 h occur at the western and north- representsrepresents the lengths the lengths of the of availablethe available data data series, series, whereas whereas different different colours present present the the range range of values. of values. ern part of the region (Figures 4 and 5 right). This could be due to the openness of the northwestern part of the domain to prevailing weather systems coming from the Atlantic. Additionally, ﬂat continental terrain warms up more compared to surrounding mountainous and coastal areas which may additionally enhance convection processes. Similar or slightly smaller values appear for the mid-long data (31–50 year) series in Slovenia, Croatia and Romania. The datasets longer than 50 years (largest dots) indicate lower mean 1-h maxima in Transylvania, than outside the southern Carpathians. The highest annual average of 1-h precipitation exceeds 26 mm there. No clear spatial pattern or differences between the periods examined can be observed in the map of maxima of the 1-h totals (Figure3, right). Large values, above 60 mm occur in all three examined periods, less in Czechia, but mainly in the middle of the region. Regarding Rx3hr and Rx6hr, the average annual maxima mainly occur in the central part of the region Figure 3. Annual average(Figures of 1-h maxima4 and 5(Rx1hr) left, HU (left and) and RO)the maximum while the 1-h absolute precipitation maxima (right). exceedingThe size of the 85 markers mm in 3 h and represents the lengths of100 the mmavailable in 6 data h occur series, at whereas the western different and colours northern present partthe range of the of values. region (Figures4 and5 right).

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Figure 4. Annual average of 3-h maxima (Rx3hr) (left) and the maximum 3-h precipitation (right). The circles and their Figure 4. Annual average of 3-h maxima (Rx3hr) (left) and the maximum 3-h precipitation (right). The circles and their sizes are the same as in Figure3. sizes are the same as in Figure 3.

FigureFigure 5. Annual 5. Annual average average of 6-h of 6-h maxima maxima (Rx6hr) (Rx6hr) (left (left)) and and thethe maximummaximum 6-h 6-h precipitation precipitation (right (right). The). Thecircles circles and their and their sizes aresizes the are same the same as in as Figure in Figure3. 3.

The maps of the seasonal averages of 1-h precipitation intensity (SPII1hr, Figure 6) reveal that the hourly precipitation is most intense in summer (JJA) due to the increased convective activity. The winter (DJF) intensity hardly reaches 1 mm/h; the spring (MAM) and autumn (SON) values are around 1 mm or slightly above; and the summer means exceed the 1.6 mm/h at a third of the gauges in the region. The results are consistent across the region irrespective to the period of the analysis. Comparing the corresponding mean 1-, 3- and 6-h intensities, the largest values in summer and the lowest in winter are obvious for each sub-total (Figures 7 and 8). In DJF and SON season somewhat-higher values are obtained in the south-western part of the domain (CRO) than in the rest of the area. This might be because of moist Adriatic air advection from southern directions, while air coming from northern directions over the continent is relatively dry. Similar can be seen for southern Romanian area as a result of Black Sea in the vicinity as a source of moisture and complex terrain, higher SPII1hr values are found on the windward side of the mountain compared to the lee.

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Figure 4. Annual average of 3-h maxima (Rx3hr) (left) and the maximum 3-h precipitation (right). The circles and their sizes are the same as in Figure 3.

Atmosphere 2021, 12, 838 9 of 18 Figure 5. Annual average of 6-h maxima (Rx6hr) (left) and the maximum 6-h precipitation (right). The circles and their sizes are the same as in Figure 3.

The maps of the seasonal averages of 1-h precipitation intensity (SPII1hr, Figure 66)) reveal that the hourly precipitation is most intense in summer (JJA) due to the increased convective activity. The winter (DJF) intensityintensity hardlyhardly reachesreaches 11 mm/h;mm/h; the the spring (MAM) and autumn (SON) values are around 1 mm or slightly above; and the summer means exceed thethe 1.61.6 mm/h mm/h at at a a third third of of the the gauges gauges in the in region.the region. The resultsThe results are consistent are consistent across acrossthe region the region irrespective irrespective to the periodto the period of the analysis.of the analysis. Comparing Comparing the corresponding the corresponding mean 1-,mean 3- and1-, 3- 6-h and intensities, 6-h intensities, the largest the largest values invalues summer in summer and the and lowest the in lowest winter in are winter obvious are obviousfor each for sub-total each sub-total (Figures 7(Figures and8). 7 In and DJF 8). and In SONDJF and season SON somewhat-higher season somewhat-higher values valuesare obtained are obtained in the south-westernin the south-western part of part the of domain the domain (CRO) (CRO) than inthan the in rest the of rest the of area. the area.This mightThis might be because be because of moist of Adriatic moist Adriatic air advection air advection from southern from southern directions, directions, while air whilecoming air from coming northern from directionsnorthern directions over the continent over the iscontinent relatively is dry.relatively Similar dry. can Similar be seen can for besouthern seen for Romanian southern area Romanian as a result area of as Black a result Sea inof theBlack vicinity Sea in as the a sourcevicinity of as moisture a source and of moisturecomplex terrain,and complex higher terrain, SPII1hr higher values SPII1hr are found values on theare windwardfound on the side windward of the mountain side of thecompared mountain to the compared lee. to the lee.

AtmosphereAtmosphere 2021 2021, 12, 12, x, xFOR FOR PEER PEER REVIEW REVIEW 1010 of of 19 19

FigureFigure 6. 6.Seasonal Seasonal (DJF–winter, (DJF–winter, MAM–spring, MAM–spring, JJA–summer,JJA–summer, SON–autumn) SON–autumn) averagesaveragesof of the the 1-h 1-h precipitation precipitation intensity intensity Figure 6. Seasonal (DJF–winter, MAM–spring, JJA–summer, SON–autumn) averages of the 1-h precipitation intensity (SPII1hr).(SPII1hr). The The circles circles and and their their sizessize sizes s are are thethe the samesame same asas as inin in FigureFigure Figure3 3.. 3.

Figure 7. Cont.

FigureFigure 7. 7. Seasonal Seasonal averages averages of of the the 3-h 3-h precipitation precipitation intensity intensity (SPII3hr (SPII3hr).). The The circles circles and and their their sizes sizes are are the the same same as as in in Figure Figure 3.3.

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Figure 6. Seasonal (DJF–winter, MAM–spring, JJA–summer, SON–autumn) averages of the 1-h precipitation intensity (SPII1hr).Figure 6. TheSeasonal circles (DJF–winter, and their size MAM–spring,s are the same JJA–summer, as in Figure 3.SON–autumn) averages of the 1-h precipitation intensity (SPII1hr). The circles and their sizes are the same as in Figure 3.

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Figure 7. Seasonal averages of the 3-h precipitation intensity (SPII3hr). The circles and their sizes are the same as in Figure Figure3. 7. Seasonal averages ofof thethe 3-h3-h precipitationprecipitation intensity intensity (SPII3hr). (SPII3hr). The The circles circles and and their their sizes sizes are are the the same same as as in in Figure Figure3. 3.

Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 19

Figure 8. Seasonal averages of thethe 6h6h precipitationprecipitationintensity intensity (SPII6hr). (SPII6hr). The The circles circles and and their their sizes sizes are are the the same same as as in in Figure Figure3. 3. The result of the trend analysis for the mean 1-h precipitation intensity (SPII1hr) can be seenThe inresult Figure of the9. Fortrend the analysis sake of for consistency, the mean 1-h the precipitation estimates of intensity trend and (SPII1hr) the test can of bestatistical seen in significanceFigure 9. For was the performedsake of consiste for thency, common the estimates period availableof trend and from the 1998. test Dueof sta- to tisticalthe lack significance of DJF observations was performed in Romania for the and common in Serbia period the trend available estimates from for 1998. winter Due are to thenot lack available of DJF for observations these countries. in Romania The results and in revealed Serbia the an increasingtrend estimates trend for in thewinter winter are not1-h intensityavailable concentratedfor these countries. to the westernThe results bound revealed of the an domain increasing with trend significant in the increase winter 1-hon fiveintensity stations concentrated (circled triangles). to the western The summer bound SPII1hr of the domain increased with in 70%significant of the analysedincrease onseries. five Thestations largest (circled rise exceedstriangles). 0.2 The mm/decade, summer SPII1hr although increased only 13% in 70% of the of gaugesthe analysed show series.significant The change.largest rise Decreasing exceeds 0.2 values mm/decade, appear as although well, rather only but 13% not of exclusivelythe gauges inshow the significantsouthwestern change. part ofDecreasing the domain values and out appear of the as southeastern well, rather Carpathians. but not exclusively Intensification in the southwesternof the autumn part 1-h rainfallof the domain depth (0.15 and mm/decadeout of the southeastern or more) is Carpathians. found in 67% Intensification of the stations of(16% the ofautumn them statistically 1-h rainfall significant),depth (0.15 exceptmm/decade in the or eastern more) regionsis found of in Romania. 67% of the A stations mixture (16%of decreasing of them statistically and increasing significant), change wasexcept found in the in theeastern spring regions trend of pattern. Romania. Apositive A mix- turetrend of for decreasing some gauges and ( 5increasing stations in change total) with was significantfound in the change spring appears trend inpattern. Austria A and posi- in tiveSlovakia, trend rangingfor some from gauges 0.15 (5 to stations 0.2 mm/decade, in total) with and significant the same rate change but negative appears appearsin Austria in and in Slovakia, ranging from 0.15 to 0.2 mm/decade, and the same rate but negative appears in Croatia and in Serbia (8 stations in total). A dipole pattern with an increase in northern and decrease in the southern part of the domain may be related to findings from other studies where a northward shift in storm track trajectories is detected, especially for Vb situations which are related to heavy precipitation events [53]. The result of the trend analysis for the mean 1-h precipitation intensity (SPII1hr) can be seen in Figure 9. For the sake of consistency, the trend estimates and the test of statistical significance was performed for the common period available from 1998. Due to the lack of DJF observations in Romania and in Serbia the trend estimates for winter are not available for these countries. In the last 20 years, the summer North Atlantic Oscillation (NAO) index has generally negative and has a decreasing trend; these results support findings that during such conditions the southern European area tends to be drier, while northern is wetter than normal (e.g., [45,54]). Still, it can be seen that the rate of the northern positive trend is generally stronger compared to the southern decreasing one which may indicate that there is an additional contribution to these changes. Besides changes in North Atlantic storm tracks, climate warming-related feedback processes are also one of the contributors to variations in extreme precipitation [55,56]. An increase in extreme precipitation due to general increase in air temperature, would result in a pattern observed here; more positive trend in southern area and less negative in northern part. However, this assumption needs to be investigated in detail. For example, Cheval et al. [32] showed that thermodynamic factors, such as air temperature and humidity are the major drivers for summer precipitation in the southeastern Carpathians, so that one can assume that local conditions have a major role in intense precipitation over the area, while NAO is an important triggering factor for cold season precipitation.

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Croatia and in Serbia (8 stations in total). A dipole pattern with an increase in northern and decrease in the southern part of the domain may be related to ﬁndings from other studies where a northward shift in storm track trajectories is detected, especially for Vb situations which are related to heavy precipitation events [54]. In the last 20 years, the summer North Atlantic Oscillation (NAO) index has generally negative and has a decreasing trend; these results support ﬁndings that during such conditions the southern European area tends to be drier, while northern is wetter than normal (e.g., [46,54]). Still, it can be seen that the rate of the northern positive trend is generally stronger compared to the southern decreasing one which may indicate that there is an additional contribution to these changes. Besides Atmosphere 2021, 12, x FOR PEER REVIEWchanges in North Atlantic storm tracks, climate warming-related feedback processes are12 of 19

also one of the contributors to variations in extreme precipitation [55,56]. An increase in extreme precipitation due to general increase in air temperature, would result in a pattern observed here; more positive trend in southern area and less negative in northern part. However,We acknowledge this assumption that needs the period to be investigated available for in detail. trend For analysis example, is Chevalrelatively et al. short, [19] so it willshowed be interesting that thermodynamic to update these factors, results such as in air the temperature coming years and humidityto see if arethe theresults major reflect saydrivers decadal for variation summer precipitation or a long-term in the underlying southeastern trend. Carpathians, Important so that to note one can here assume is that not onlythat the local anthropogenic conditions have contributions a major role in can intense induce precipitation trends in over the the sub-daily area, while rainfall NAO isdepths butan also important the natural triggering variability factor forin the cold climate season precipitation.system.

FigureFigure 9. Decadal 9. Decadal change change of of the the mean mean 1-h1-h precipitationprecipitation intensity intensity (SPII1hr). (SPII1hr). We acknowledge that the period available for trend analysis is relatively short, so it 5.2.will Some be interestingIndices for toSelected update Stations these results in the coming years to see if the results reﬂect say decadalTo complete variation the or apicture long-term drawn underlying in the trend.previous Important section to notesome here additional is that not indices only are illustratedthe anthropogenic for 12 selected contributions stations. can induceThe principle trends in theof sub-dailythe station rainfall selection depths was but alsoto cover climaticthe natural sub-regions variability of in the the Pannonian climate system. basin (Figure 1 right panel) on one hand and to include stations from all the countries contributed to this paper on the other hand. We selected one index from each group of the main types of indices to this initial study. One from the “frequency/threshold” type indices: R3hr20mm, one from the “duration” type indices: MxLWS and the MoWH from the “Diurnal cycle” type indices. We have chosen the 3-h rainfall depth to analyse in this section instead of 1-h sum, as the 1-h sampling frequently cuts the intense rainfall events into two parts. The 20 mm threshold was chosen as it specifies a heavy rainfall event which is not extremely rare to analyse. In the half of the examined years at least one event was detected when the 3-h subtotal exceeded the 20-mm threshold. The indices R3hr20 mm and MxLWS are similar to the daily precipitation indices [50], while the MoWH is useful for the modelling community [48]. Wet hours are defined as ≥0.1 mm, acknowledging that not all instruments measure to this precision. The graphs in Figure 10 are ordered by the number of the climatic sub-regions. We note that the time periods are not necessarily equal. The complete data series for the chosen stations were analysed to exploit most of the properties of the hourly or multi-hourly precipitation indices series. The indices R3hr20mm represented by columns (Figure 10 to the left axis on the left panel) show high variability on the selected stations. Rather variability than change can

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5.2. Some Indices for Selected Stations To complete the picture drawn in the previous section some additional indices are illustrated for 12 selected stations. The principle of the station selection was to cover climatic sub-regions of the Pannonian basin (Figure1 right panel) on one hand and to include stations from all the countries contributed to this paper on the other hand. We selected one index from each group of the main types of indices to this initial study. One from the “frequency/threshold” type indices: R3hr20mm, one from the “duration” type indices: MxLWS and the MoWH from the “Diurnal cycle” type indices. We have chosen the 3-h rainfall depth to analyse in this section instead of 1-h sum, as the 1-h sampling frequently cuts the intense rainfall events into two parts. The 20 mm threshold was chosen as it specifies a heavy rainfall event which is not extremely rare to analyse. In the half of the examined years at least one event was detected when the 3-h subtotal exceeded the 20-mm threshold. The indices R3hr20 mm and MxLWS are similar to the daily precipitation indices [51], while the MoWH is useful for the modelling community [49]. Wet hours are defined as ≥0.1 mm, acknowledging that not all instruments measure to this precision. The graphs in Figure 10 are ordered by the number of the climatic sub-regions. We note that the time periods are not necessarily equal. The complete data series for the chosen stations were analysed to exploit most of the properties of the hourly or multi-hourly precipitation indices series. The indices R3hr20mm represented by columns (Figure 10 to the left axis on the left panel) show high variability on the selected stations. Rather variability than change can be explored in the frequency of 3-h aggregation above 20 mm. The most cases were registered in Beograd (Serbia) and Daruvar (Croatia): eight and seven respectively, which can be explained by the influence of the Mediterranean climate. Years with zero cases occurred at all stations in the examined period. The fewest cases occurred in Little Plain in Hungary (Mosonmagyaróvár) and at the Romanian stations, the latter in accordance with the growing continental character there. The shortest MxLWS can be seen in the Romanian stations; between 18–20 h on average for the examined periods (Figure 10. left panel, line with marker). The maxima crossed 50 h on the stations representing the south-western part of the region in Slovenia and Croatia. The wettest hours for each month are indicated in the right panel of the Figure 10. The wettest hours appear from late afternoon to night-time in the warm season (May-September), as expected. Despite the relatively short time series of Szeged, the wettest hours separated well from early evening to dawn during the year. The wettest hours are grouped from early to late afternoon for Beograd from March to August, and around midnight during the fall months. We note that measuring was interrupted at Beograd in wintertime. The first insight to the wettest hours on the selected stations show that the range of the wettest hours extends from late afternoon to midnight during the year. The wettest hours happen typically not earlier than 2 pm at Miercurea Ciuc station in Romania, the most eastern station of the domain that we have included in this study. Atmosphere 2021, 12, 838 13 of 18

Figure 10. Cont. Atmosphere 2021, 12, 838 14 of 18

Figure 10. R3hr20mm (count of 3-h periods with greater than 20 mm; left panel, columns to the left axis) and MxLWS (Maximum length of wet spell, left panel, lines with marker to the right axis) and MoWH (timing of the wettest hour) on the right panel. Atmosphere 2021, 12, 838 15 of 18

6. Conclusions One of the purposes of this paper was to explore the sub-daily precipitation data availability in the data archives of the National Meteorological and Hydrological Services (NMSs) in the PannEx region (Pannonian Basin Experiment), which is a GEWEX-related Regional Hydroclimate Project in the Pannonian/Carpathian basin. Through the collaboration of eight NMSs in the PannEx region and the INTENSE project software developed by the team at Newcastle University was applied for quality control of the hourly data and for derivation of sub-daily precipitation indices. As a result of this common effort, numerous derived statistics for 1-hourly, 3-hourly and 6-hourly rainfall depths have been produced for further analysis. Our aim was to assess some of the basic climatological properties of the sub-daily precipitation dataset collected in this initial analysis. Large spatial variability of the mean of the annual maxima and the absolute maxima of 1-h precipitation was observed in the region in the examined period. The highest mean annual maxima values around 25 mm appear in the middle part of the Pannonian basin. The maxima of the 1-h precipitation period exceeding 60 mm falls in the middle part of the region. The spatial distribution of the aggregated 3-h and 6-h maxima (annual mean and maximum) is similar to the 1-h sum, while the absolute maxima exceeds the 85 mm in 3 h and the 100 mm in 6 h at the western and northern part of the region. The summer (JJA) hourly and multi hourly precipitation is the most intense in the PannEx region, as expected. The influence of the Adriatic Sea in the southwestern part of the domain and the effect of the Black Sea in the southeastern part of the domain resulted in higher winter (DJF) and autumn (SON) hourly precipitation intensity. The seasonal trends of the 1-h precipitation intensity were analyzed from 1998. The summer hourly precipitation intensity increased in the largest extent, at 70% of the analysed series in the region with above 0.2 mm/decade, although only 13% of the gauges show statistically significant change. The autumn 1-h rainfall depth intensified substantially, but less than the summer, overall at 67% of the stations there is an increase and 16% of them are statistically significant. It was noted that not only anthropogenic contributions can induce trends in the sub-daily rainfall depths but also natural variability in the climate system. Some of the indices for selected stations from different climatic regions of the Pan- nonian Basin are illustrated on graphs. Regarding the yearly occurrence of 3-h periods greater than the 20 mm threshold, a large variability in time was found. The influence of the Mediterranean climate was evident in more common occurrences (7–8 cases per year) at southern stations and, contrarily, a growing continental character was found by fewer occurrences at stations located in the eastern part of the domain. The annual maxima of the maximum length of wet hours are the fewest at the Romanian stations, between 18–20 h on average. The maximum crossing 50 h appears on the stations located at the southwestern part of the PannEx region. The wettest hours in the region range from the late afternoon to midnight. However, further study, including more stations, is required to conclude the main features of this index. Overall, in this study, we provided an initial climate analysis of sub-daily precipitation through collaboration within the PannEx initiative. One of the main aims was to contribute to enlarging the data availability for regional and global analysis of sub-daily precipitation extremes. It is important to note that sub-daily precipitation indices were analyzed here for the PannEx region, but the participating NMHSs offered more indices series to the global indices dataset. Our plan is to involve the missing part of the PannEx region, namely the Tran- scarpathia, to this activity to cover the Pannonian basin and its surroundings as a whole. Including longer time series to the analysis of sub-daily precipitation indices is necessary to obtain reliable conclusions regarding the changes. The ongoing data rescue activity at NMHSs could support the lengthening the derived indices and including more stations to the investigations. Overcoming limitations imposed by short or non-existent records of sub-daily precipitation regional frequency analysis can be performed to identify ho- mogenous extreme precipitation regions. Regionalization requires involving more climate variables and circulation types into analyses. The collaboration of the NMHSs evolved Atmosphere 2021, 12, 838 16 of 18

during this work provide an excellent framework for further analysis of the behavior of short-term extreme precipitation in the PannEx region.

Author Contributions: Conceptualization, M.L., O.S., K.C.K., A.D., S.C.; methodology, M.L., O.S., K.C.K., A.D., S.C.; software development, D.P.; software running, M.L., A.D., A.I., I.N., K.K., D.M., P.S., P.K., D.P., validation, M.L., O.S., A.D., A.I., K.K., B.C., I.N., P.S., A.F., P.K., P.P., D.P.; formal analysis, M.L., A.D., A.I., K.C.K., I.N., S.C., K.K.; investigation, M.L., A.D.; data preparation, M.L., K.K., P.S., P.K., A.D., P.P., I.N.; writing—original draft preparation, M.L., K.C.K., I.N., A.D.; K.K. writing—review and editing, A.D., S.C.; D.P., B.C., K.C.K., I.N.,K.K., D.M., visualization, O.S.; supervision, M.L., K.M., K.C.K., B.C., D.M., A.D., S.C. All authors have read and agreed to the published version of the manuscript. Funding: The INTENSE project is supported by the European Research Council (ERC-2013-CoG- 617329). The Czech contribution to this paper was supported by the Ministry of Education, Youth and Sports of the Czech Republic for SustES–Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions project, ref. CZ.02.1.01/0.0/0.0/16_019/0000797. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The hourly precipitation data used in this study are stored locally at the National Meteorological and Hydrological Services contributed to this paper. Acknowledgments: The authors would like to thank the PannEx Regional Hydroclimate Project (RHP) for the scientific support of our research and to the team at Newcastle who have contributed to other aspects of the indices work: Elizabeth Lewis, Hayley Fowler and Stephen Blenkinsop. The authors would like to express their gratitude to the three anonym reviewers for their detailed and valuable reviews. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, and interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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