摘要:基于中国均一化气温数据集CN05.1的观测数据，结合暖昼指数（TX90）、冷昼指数（TX10）、暖夜指数（TN90）、冷夜指数（TN10）、暖日持续指数（WSDI）和冷日持续指数（CSDI）6个极端温度指数，从气候平均、概率分布、年际变率和年际趋势方面，系统评估区域气候模式（Climate–Weather Research and Forecasting model, CWRF）对1980～2015年间我国极端温度指数区域分布和年际变化的模拟能力，为改进并利用模式研究我国未来区域极端温度的预测提供科学依据。结果显示：观测的冷暖指数在北方的年际变率幅度高于南方，其中暖指数在我国大部分地区为增暖趋势，冷指数在北方地区的变冷趋势显著，尤其暖夜增暖、冷夜变冷，极端暖事件（WSDI）的持续性比冷事件（CSDI）显著。CWRF模式较好再现了极端温度指数的年均分布和年际变化趋势特征，尤其对暖日和冷日持续指数的模拟优势显著，但仍存在系统性的区域偏差，如低估暖昼和冷夜的极值强度；对华东地区暖（冷）指数变暖（冷）的趋势存在低（高）估；尤其是低估青藏高原地区暖、冷指数的强度，并且高估其暖昼变冷、暖夜变暖的年际变化趋势。因此，该模式对华东及高原地区极端温度的强度和年际变率的模拟仍亟需改善。
关键词:极端温度指数/
区域气候模式/
时空分布/
年际趋势/
模拟评估
Abstract:This study evaluates the capability of the regional Climate-Weather Research and Forecasting model (CWRF) in simulating the spatiotemporal variations of daily extreme temperature indices from 1980 to 2015 over eight key regions in China based on the homogenization temperature dataset (CN05.1) to provide a scientific basis for improving the model in predicting regional extreme temperatures in China. In this study, we focus on the four percentile-based threshold indices (TX90, TX10, TN90, and TN10) and two duration indices (warm spell duration indicator, WSDI and cold spell duration indicator, CSDI) defined by an expert team on climate change detection and indices. From observation, the annual mean distributions of extreme indices show distinct regional features, and extreme warm events persist longer than extreme cold events. Both warm and cold indices have larger interannual variability in the north, but the warm index shows a warmer trend in most parts of China and the cold index shows a colder trend in North China, where has a more pronounced changing trend of warm and cold nights. The CWRF generally reproduces the observed annual mean distributions of these extreme indices, especially exhibiting superiority in simulating extreme events duration indices, and well simulates the interannual variability and changing trends of extreme indices in most regions. However, several regional biases still exist. For instance, CWRF underestimates the intensity of extremely warm days and cold nights but overestimates WSDI in Central East China and CSDI in Northwest China. In the Qinghai–Tibet Plateau, CWRF tends to underestimate both indices but overestimates the cooling and warming trends of warm days and cold nights, respectively. In East China, CWRF underestimates the cooling trend of cold nights but overestimates the warming trend of warm days and nights.
Key words:Extreme temperature indices/
Regional climate model/
Spatio–temporal distribution/
Interannual trend/
Simulation assessment
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