Spatiotemporal variability of extreme precipitation in east of northwest China and associated large-scale circulation factors

Abstract

Spatial and temporal distributions and influencing factors of extreme precipitation are important bases for coping with future climate change. The spatiotemporal variability and affecting factors of extreme precipitation indices (EPIs) in East of northwest China (ENW) during 1961-2015 were investigated using a series of approaches such as modified Mann-Kendall trend test, Hurst exponent, Ensemble empirical mode decomposition (EEMD), and geodetector model. The results showed that CDD and CWD decreased significantly (P<0.01), with rates of 1.4 days/decade and 0.07 days/decade, respectively. EPIs in ENW exhibited an obvious heterogeneity. CDD gradually increased from the southeast to the northwest. The remaining EPIs generally showed the opposite trend. Some stations in ENW may experience more extreme precipitation events in the future. Geodetector results demonstrated that large-scale circulation factors had a significant impact on EPIs in ENW. The influence of large-scale climate factors on EPIs was concentrated in nonlinear enhancement, and Nino3.4 and SO were the dominant driving factors that played a major role in the variability of EPIs. The results of this study provided a reference for ENW and other arid and semiarid regions to cope with extreme climates and develop corresponding strategies.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

“…Climate change significantly impacts the increase in extreme precipitation events. In the future, a significant proportion of meteorological stations may encounter more intense extreme precipitation events than have been recorded historically [79]. The non-stationarity of precipitation may result in previously gauged stations becoming datasparse regions, increasing our dependence on precipitation products [94].…”

“…According to the standard set by the Expert Team on Climate Change Detection and Indices, two key variables were employed to ascertain the precision of extreme precipitation events: annual maximum 1-day precipitation amount (Rx1) and annual maximum 5-day precipitation amount (Rx5). The relative errors of gridded global precipitation and measured precipitation (PBias-Rx1 and PBias-Rx5) were calculated as evaluation metrics of extreme precipitation estimation accuracy [79]. Besides KGE, the most widely used Nash-Sutcliffe Efficiency (NSE) was also used for hydrological evaluation [80].…”