High-Resolution Projections of Mean and Extreme Precipitation over China by Two Regional Climate Models

In this study, we employ two regional climate models (RCMs or RegCMs), which are RegCM4 and PRECIS (Providing Regional Climates for Impact Studies), with a horizontal grid spacing of 25 km, to simulate the precipitation dynamics across China for the baseline climate of 1981-2010 and two future climates of 2031-2060 and 2061-2090. The global climate model (GCM)-Hadley Centre Global Environment Model version 2-Earth Systems (HadGEM2-ES) is used to drive the two RCMs. The results of baseline simulations show that the two RCMs can correct the obvious underestimation of light rain below 5 mm day(-1) and the overestimation of precipitation above 5 mm day(-1) in Northwest China and the Qinghai-Tibetan Plateau, as being produced by the driving GCM. While PRECIS outperforms RegCM4 in simulating annual precipitation and wet days in several sub-regions of Northwest China, its underperformance shows up in eastern China. For extreme precipitation, the two RCMs provide a more accurate simulation of continuous wet days (CWD) with reduced biases and more realistic spatial patterns compared to their driving GCM. For other extreme precipitation indices, the RCM simulations show limited benefit except for an improved performance in some localized regions. The future projections of the two RCMs show an increase in the annual precipitation amount and the intensity of extreme precipitation events in most regions. Most areas of Southeast China will experience fewer number of wet days, especially in summer, but more precipitation per wet day (>= 30 mm day(-1)). By contrast, number of wet days will increase in the Qinghai-Tibetan Plateau and some areas of northern China. The increase in both the maximum precipitation for five consecutive days and the regional extreme precipitation will lead to a higher risk of increased flooding. The findings of this study can facilitate the efforts of climate service institutions and government agencies to improve climate services and to make climate-smart decisions.