Abstract
Abstract. The spatiotemporal variability of rainfall in the dry (October–March) and wet (April–September) seasons over eastern China is examined from 1901–2016 based on the gridded rainfall dataset from the University of East Anglia Climatic Research Unit. Principal component analysis is employed to identify the dominant variability modes, wavelet coherence is utilized to investigate the spectral features of the leading modes of precipitation and their coherences with the large-scale modes of climate variability, and the Bayesian dynamical linear model is adopted to quantify the time-varying correlations between climate variability modes and rainfall in the dry and wet seasons. Results show that first and second principal components (PCs) account for 34.2 % (16.1 %) and 13.4 % (13.9 %) of the variance in the dry (wet) season, and their variations are roughly coincident with phase shifts of the El Niño–Southern Oscillation (ENSO) in both seasons. The anomalous moisture fluxes responsible for the occurrence of precipitation events in eastern China exhibit an asymmetry between high and light rainfall years in the dry (wet) season. The ENSO has a 4- to 8-year signal of the statistically positive (negative) association with rainfall during the dry (wet) season over eastern China. The statistically significant positive (negative) associations between the Pacific Decadal Oscillation (PDO) and precipitation are found with a 9- to 15-year (4- to 7-year) signal. The impacts of the PDO on rainfall in eastern China exhibit multiple timescales as compared to the ENSO episodes, while the PDO triggers a stronger effect on precipitation in the wet season than the dry half year. The interannual and interdecadal variations in rainfall over eastern China are substantially modulated by drivers originated from the Pacific Ocean. During the wet season, the ENSO exerted a gradually weakening effect on eastern China rainfall from 1901 to 2016, while the effects of the PDO decreased before the 1980s, and then shifted into increases after the 2000s. The finding provides a metric for assessing the capability of climate models and guidance of seasonal prediction.
Highlights
As a densely populated area with lots of industrial and agricultural activities, eastern China is frequently affected by catastrophic floods and droughts due to the variability of precipitation events (Liu et al, 2015; Gao and Xie, 2016; Huang et al, 2017; Yang et al, 2017a; Luo and Lau, 2018; Ge et al, 2019)
We present the following analyses in the wet and dry seasons, respectively, to provide a concise result
Space–time variability of rainfall during the dry and wet seasons over eastern China is examined by utilizing principle component analysis (PCA), wavelet coherence and Bayesian dynamic linear model (BDLM) based on the Climatic Research Unit (CRU) gridded and observed rainfall datasets
Summary
As a densely populated area with lots of industrial and agricultural activities, eastern China is frequently affected by catastrophic floods and droughts due to the variability of precipitation events (Liu et al, 2015; Gao and Xie, 2016; Huang et al, 2017; Yang et al, 2017a; Luo and Lau, 2018; Ge et al, 2019). Deficient precipitation in northern China caused a severe drought of 226 d without stream discharge over the Yellow River basin (Qian and Zhou, 2014; Xu et al, 2015; Zhang and Zhou, 2015). It is of great importance to investigate the rainfall variability in eastern China and its associated physical mechanisms
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