中文摘要：

区域气候模拟研究在过去十几年里取得了显著的进步。经过广泛的发展和不断的检验，区域气候模式现在已经成为气候研究和业务预报的重要工具。目前已经发表了很多令人鼓舞的结果，其中包括过去极端气候事件的模拟，当前气候发展演变和未来气候变化的预测，特别是对月和季节尺度气候的模拟与预测。通过对高分辨率和动力连续的区域气候模式结果的分析，人们对于周一季节时间尺度的各种物理过程，包括陆面和水文过程、边界层、云和降水、云-辐射相互作用的认识也在不断的深入。然而，区域气候是多尺度扰动（如中尺度、天气尺度、行星尺度扰动）和多圈层系统（如大气圈、生物圈、水圈、冰雪圈、陆面）相互作用的结果，同时物理过程本身具有不确定性，人们对一些复杂的物理过程，特别是土壤湿度作用以及云-气候反馈过程也缺乏深刻的理解，因此该领域的研究还面临着很多挑战。作者重点总结并评述了区域气候模式对现在和未来区域气候模拟、极端天气和气候事件模拟、物理过程研究、短期气候预测几方面应用的研究进展，最后讨论了区域气候模式发展在上述各方面，特别是周一次季节时间尺度区域天气和气候的模拟与预测所面临的挑战和应用前景。

英文摘要：

Significant advances have been made in merous regional climate models （RCMs） have now regional climate modeling research during the past decades. Nubecome an important tool for climate research and operational prediction after extensive model developments and verifications. Many encouraging results have been published in simulating historic extreme climate events and predicting our future climate, particularly in the monthly to seasonal climate variables. By analyzing the high-resolution, dynamically consistent model datasets, remarkable improvements have been made in understanding various physics processes, such as land-surface and hydrological processes, the planetary boundary layer, clouds and precipitation, cloud-radiation interaction at the weekly to seasonal scales. However, RCMs still face many challenging issues due to the complicated scale interaction over a large-range tempo- ral and spatial scales （e. g. , from mesoscale to synoptic and planetary scale disturbances）, the interaction of individual climate sub-systems （e. g. , atmosphere, biosphere, water, ice-snow, land-surface）, and the lack of understanding of some complex physical processes, especially clouds and soil moisture. This article summarizes the recent progress in the present and future climate simulation, the extreme weather and climate studies, and in understanding various physics processes as well as the short-term climate prediction using RCMs. The prospects of RCMs related to the above subjects, especially the weekly to subseasonal regional climate simulation and prediction, will also be discussed.