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

In this study, we use the cumulative distribution function transform to conduct the bias correction for simulations from different regional climate models (RCMs) driven by different global climate models (GCMs), and we evaluate the performances of the RCMs after correction on reproducing the precipitation in China during the historical period (1986–2005). Then, the future precipitation in the middle (2036–2065) and late 21st century (2066–2095) is predicted under the RCP8.5 scenario and compare the difference before and after bias correction. 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 in northern arid regions. For extreme precipitation and different rainfall levels, the root mean squared errors of most indexes are reduced by about 90%, and the correlation coefficients are quite close to 1. For future precipitation, bias correction method can reduce the overestimation of RCM simulations, but cannot change the trends of precipitation variation. Compared with the simulations before bias correction, the predicted future precipitation presents some differences in different regions. After correction, the spread of the precipitation and the most extreme precipitation indexes are smaller than those before correction. The predicted future daily precipitation intensity is 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. The simulations from the same RCM driven by different GCMs are generally more consistent than those of different RCMs driven by one GCM. Therefore, the projection of climate change will be more reliable if we use different GCMs to drive the same RCM and then correcting bias.