Quantifying spatiotemporal influences of climate index on seasonal extreme precipitation based on hierarchical Bayesian method

Quantifying spatiotemporal influence of climate index on extreme precipitation will help to better understand the variability of extreme precipitation. The extreme precipitation is usually influenced by different climate indices, and mutual offset is unavoidable to occur, thus the rotated empirical orthogonal function was used to identify the different influences of climate indices on extreme precipitation in space and time. The variation of extreme precipitation in data-scarce region is also concerned, hence, an improved spatiotemporal regional frequency analysis model was further developed, therein the identified spatiotemporal influences of climate indices on extreme precipitation were quantified using Bayesian hierarchical method. In this study, the in situ seasonal maximum one-day precipitation amount (Rx1day) was used to represent seasonal precipitation extremes from 1957 to 2010 in the Poyang Lake basin, and spatiotemporal influences of El Nino-Southern Oscillation (ENSO), North Atlantic oscillation (NAO) and Indian Ocean Dipole (IOD) on seasonal Rx1day were quantified. Results indicated that the seasonal Rx1day was influenced by different climate indices in the Poyang Lake basin, ENSO tends to affect spring and autumn Rx1day, IOD tends to affect summer Rx1day, and NAO tends to affect spring and winter Rx1day. The response of extreme precipitation on climate index is varied in different regions, and this was well distinguished and verified, such as negative ENSO (in the same year) events tends to cause spring Rx1day slight decrease in the southern part of the basin while increase about 15% in the northern part with center around the Poyang lake.