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The changes in mean and extreme climate in China during 2020–2060 are detected with both Weather Research and Forecasting and RegCM4, by downscaling the simulations from EC‐EATTH and IPSL‐CM5A under both the RCP4.5 and RCP8.5 scenarios. The climate changes under the two scenarios exhibit similar patterns, with stronger intensity under the RCP8.5 scenario. For the mean precipitation, increases are projected in most regions, with the largest relative increase in the Tarim Basin. Slight drought mainly occurs in the south‐eastern part of China. The frequency of drizzle rain is expected to decrease in all the sub‐regions, but the moderate to heavy rainfall as well as the storm would occur more frequently, especially on the Tibetan Plateau. The whole country would experience much warmer climate in the future, with the strongest warming over the Tibetan Plateau. By detecting the changes in climate extremes, it is indicated that less dry extremes would occur in the wet areas of China, while more dry events in the arid and semiarid regions. The wet extreme indices would increase in most regions, especially in the wet areas. The surface air temperature tends to become extremely warmer in the future over the whole country, with the strongest change over the Tibetan Plateau. The changes in mean and extreme climate depend strongly on the driving global climate models, with wetter and warmer climate in the downscalings over IPSL‐CM5A, and the model physics of the regional climate models also exert great impact on the projections. Finally, the possible mechanisms for the changes of extreme precipitation are discussed. The enhanced summer monsoon in the future transports more moisture to China, which could lead to more summer precipitation. As a result, the wet extremes tend to increase.

Based on the Coordinated Regional Downscaling Experiment‐East Asia second phase (CORDEX‐EA‐II) with higher resolution, model results driven by ERA‐Interim reanalysis using WRF, RegCM4 and CCLM are evaluated against the observational datasets including CN05.1, CRU and GPCP during the period of 1989–2009. The results show that the RCMs have the capability to simulate the annual and seasonal mean surface air temperature and precipitation, however, some biases are produced. The biases are highly dependent on the geophysical locations and the RCMs applied, and CCLM agrees better with the observed precipitation over ocean. CCLM also outperforms the other two RCMs in simulating the interannual variations of temperature and precipitation in most sub‐regions, which can be attributed to its better presentation of the interannual variation of large scale circulation. Generally, all the three RCMs can well reproduce the seasonal cycles of the surface air temperature in most sub‐regions, however, only in the northern regions of China can the RCMs well reproduce the seasonal cycles of precipitation.

Forced by two Coupled Model Intercomparison Project phase 5 (CMIP5) global models, EC‐EARTH and IPSL‐CM5A, both regional climate models (RCMs) of Regional Climate Model system version 4 (RegCM4) and Weather Research and Forecasting (WRF) are used to perform regional climate simulations over China for 1980–2000. In general, the simulations in Institute Pierre Simon Laplace‐Climate Model version 5A (IPSL‐CM5A) are improved by the downscaling of many aspects of both precipitation and surface air temperature, with improvements including the significant enhancement of the resolution and presentation of regional processes in the RCMs. However, the improvement over EC‐EARTH is limited. In addition, the added values also depend on the seasons, geophysical locations over the country and the variables presented. For the mean climates, both RCMs are able to exhibit better annual and winter precipitation measurements when downscaling IPSL‐CM5A over most areas. The superiority of the RCMs over IPSL‐CM5A for the surface air temperature is only obtained in summer. Seasonal cycles of both precipitation and surface air temperature are well simulated by all the models. The RCMs succeed in improving the seasonal cycles of precipitation over both GCMs in most sub‐regions, but the seasonal cycles of temperature are only improved over IPSL‐CM5A. In addition, the downscaling demonstrates more consistent probability distribution functions (PDFs) of precipitation with those of the observations in most sub‐regions. For extreme precipitation, the RCMs provide outstanding convective wet days (CWD) with reduced biases and enhanced presentations of spatial patterns. For the other three extreme precipitation indices, the improvement of the RCMs is relatively weak, with superior over the IPSL‐CM5A in several areas. The extreme temperature indices are better modelled by the RCMs, except for the minimum values of the daily minimum temperatures (TNn), with greater added values over IPSL‐CM5A than those over EC‐EARTH. Comparing two RCMs, WRF shows advantages in the downscaling precipitation, while RegCM4 works better for the surface air temperature.

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