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.