中文摘要：

本文利用中国区域660个站点逐日地面降水资料,评估了由IPCC（the Intergovernmental Panel on Climate Change）数据中心于2014年最新发布的15个全球气候模式（Global Climate Models,GCMs）以及多模式集合（Multi-Model Ensemble,MME）对中国降水的模拟精度。首先,从全球范围数据集中读取研究区范围内的GCMs降水模拟数据;然后,提取各个气象站点处的GCMs模拟值;其次,将GCMs在同一站点的模拟值取平均,得到MME模拟值;最后,以气象站点实际观测值为基准,对GCMs的模拟值精度进行评估。研究结果表明：IPCC AR5 GCMs 1996-2005年平均日降水模拟值偏差在中国地区的空间分布均呈现出西北向东南逐渐减小的特征,东部地区平均相对误差较小,平均相对误差较大的点主要分布在西部,但均方根误差呈现出从西北向东南增加的趋势;MRI-CGCM3有82.3%的日平均降水模拟值偏差都比较小,偏差介于-0.5到0.5之间;对于中国地区1996-2005年平均日降水量,BNU和MIROC-ESM模拟精度最低;MME模式模拟值的相关系数〉0.5、平均相对误差〈0.5和均方根误差〈4 mm的百分率均为最高,分别达到64.8%、25.8%和86.4%,偏差介于-0.5到0.5之间的比例为56.7%,说明MME对中国地区日平均降水的模拟精度优于大部分模式,MME模式可在一定程度上减少单个模式未来情景模拟的不确定性。

英文摘要：

This article estimated the precision of the precipitation simulated by 15 IPCC AR5（the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC AR5） GCMs（Global Climate Models） and the multi-model ensemble（MME）,based on the observed precipitation from 660 stations in China during 1996 to 2005. We firstly extracted the model simulation value at the corresponding position of the meteorological station, using the bilinear interpolation method, and took the average value of different models at the same station as the multi-model ensemble simulation value, then estimated the precision of the precipitation simulated by 15 IPCC AR5 GCMs and MME based on the observation of meteorological station. There were four evaluation parameters, including Corr（correlation coefficient）, Bias, MRE（Mean Relative Error）, and RMSE（Root Mean Square Error）. Results show that the biases of the average daily precipitations simulated by IPCC AR5 GCMs present a gradually downward trend from northwest to southeast, and the RMSEs show a gradually increasing trend from northwest to southeast, while MREs in the east are less than those in the west. 82.3% of the average daily precipitations simulated by MRI-CGCM3 have relatively small biases, ranging from-0.5 to 0.5. The precisions of average daily precipitations simulated by BNU and MIROC-ESM are lower than that of others. Compared with other models, the MME simulation has the largest percentages of which the correlation coefficients are more than 0.5, MREs are less than 0.5, and RMSEs are less than 4mm, which accounted for 64.8%, 25.8% and 86.4% respectively. And the percentage of the biases ranging from-0.5 to 0.5 is relatively large, which is 56.7%, indicating that the simulation precision of MME is better than that of any other GCMs, and the MME can reduce the uncertainty of a single GCM simulation in future scenarios. Therefore, it is more scientific and reasonable to select the precipitation simulated by MME as the climate change condition,while