High-Resolution Ensemble Projection of Mean and Extreme Precipitation Over China Based on Multiple Bias-Corrected RCM Simulations

Abstract

In this study, we used the cumulative distribution function transform to conduct a bias correction for simulations from different regional climate models (RCMs) driven by one global climate model (HadGEM2-ES). We divided the historical period into two time-frames, i.e., the calibration period and the validation period. These two periods are 1986–1998 and 1999–2011, respectively. We then choose the period from 1986 to 2005 as the calibration period. The data for the future 2006–2098 were revised and used to explore future climate change under the RCP8.5 scenario. The difference before and after bias correction were compared. The results show that the cumulative distribution function transform method can improve the simulation accuracy of RCM in terms of the average precipitation and seasonal precipitation can improve in north arid regions. For extreme precipitation and different rainfall levels, the root mean squared errors of most indexes are reduced by about 60–80% in China, and the correlation coefficients are close to 1. For future precipitation, the bias correction method could reduce the overestimation of RCM simulations, but cannot change trends of precipitation variation. Compared with the simulations before bias correction, the predicted future precipitation indicates some differences in different regions. After correction, the spread of the precipitation and the most extreme precipitation indexes was smaller than those before correction. The predicted future daily precipitation intensity was also smaller. The reduction of drought days in the arid areas is more than before the correction, and the increase days of R50 in the southern regions is larger than before the correction.