The spatiotemporal variations of seasonal precipitation extremes during 1963–2013 over China and the possible teleconnections with large-scale ocean–atmosphere indices are investigated by using the rotated empirical orthogonal functions, cross-correlation analysis and stepwise variable selection methods. Results show that northwest China experiences the most frequent dry events but exhibits a wetting tendency in all seasons, while in south and central China, extreme wet conditions are remarkable in seasons except for winter but a drying tendency is found in spring and autumn. Precipitation extremes in four different seasons are influenced by the different combinations of large-scale climate indices with different time lags, and their regional responses are complex when the climate indices are at different phases, such as winter heavy precipitation (R95p) in south of Nanling Mountain generally tends to be increased by the simultaneous positive El Niño–Southern Oscillation (ENSO) and sea surface temperature anomalies (SSTs) over South China Sea and positive Arctic Oscillation (AO) with 5-month lag, while summer R95p in the middle and lower reaches of Yangtze River Basin be decreased by the simultaneous negative SSTs over Bay of Bengal and positive AO with 9-month lag. Besides, the dominant climate indices identified by teleconnection analysis can partly explain the temporal changes of seasonal extreme precipitation in China, which can be confirmed by very similar fluctuation in the oscillatory patterns of eight chosen couples with higher correlation coefficient, and their explanation skill is more powerful in sensitive areas highlighted in the leading spatial modes of corresponding precipitation index. Moreover, this explanation skill generally enhances as the cumulative contribution of leading four rotated modes to total variance of the target variable over whole study area increases. In addition, the precipitation indices related to precipitation amount can be better explained than those related to wet or dry spell by identified dominant climate indices.
- empirical orthogonal functions
- seasonal precipitation extremes
- stepwise variable selection