https://doi.org/10.5194/hess-2020-102 Preprint. Discussion started: 23 March 2020 c Author(s) 2020. CC BY 4.0 License. 1 The precipitation variability of wet and dry season at the interannual 2 and interdecadal scales over eastern China (1901–2016): The impacts 3 of the Pacific Ocean 4 Tao Gao1, 4, 5, Fuqiang Cao 2, 3*, Li Dan3, Ming Li2, Xiang Gong5, and Junjie Zhan6 5 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and 6 Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of 7 Sciences, Beijing 100029, China 8 2 School of geosciences, Shanxi Normal University, Linfen 041000, China 9 3 CAS Key Laboratory of Regional Climate-Environment Research for Temperate 10 East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 11 China 12 4 College of Urban Construction, Heze University, Heze 274000, China 13 5 School of Mathematics and Physics, Qingdao University of Science and Technology, 14 Qingdao 266061, China 15 6 Shunyi Meteorological Service, Beijing, 101300, China 16 17 *Corresponding author address: Dr. Fuqiang Cao, 1 Gongyuan Road, Linfen 041000, 18 P. R. China. 19 Email:
[email protected] 20 21 1 https://doi.org/10.5194/hess-2020-102 Preprint. Discussion started: 23 March 2020 c Author(s) 2020. CC BY 4.0 License. 22 Abstract: The spatiotemporal variability of rainfall in dry (October- March) and wet 23 (April-September) seasons over eastern China is examined based on gridded rainfall 24 dataset from University of East Angela Climatic Research Unit during 1901–2016. 25 Principal component analysis is employed to identify the dominant variability modes, 26 wavelet coherence is utilized to investigate the spectral characteristics of leading 27 modes of precipitation and their coherences with the large-scale modes of climate 28 variability, and Bayesian dynamical linear model is adopted to quantify the 29 time-varying correlations between climate variability modes and rainfall in dry and 30 wet seasons.