This study evaluates global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) for their performance in simulating the climate extreme indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We compare global climatology patterns of the indices simulated by the CMIP6 models with those from HadEX3 and four reanalysis datasets and the CMIP5 multi-model ensemble using root-mean-square errors for the 1981–2000 period. Regional evaluations are conducted for 41 sub-regions, defined for the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, regional mean biases are analyzed for the 20-year return values (20RV) of the warmest day and coldest night temperatures (TXx and TNn) and annual maximum of daily precipitation (RX1day) using a Generalized Extreme Value (GEV) analysis. Results show that the CMIP6 models generally capture the observed global and regional patterns of temperature extremes with limited improvements compared to the CMIP5 models. Systematic biases like a cold bias in cold extremes over high-latitude regions remain even in stronger amplitudes. The CMIP6 model skills for the precipitation intensity and frequency indices are also largely comparable to those of CMIP5 models, but precipitation intensity simulations are found to be improved with reduced dry biases. The GEV analysis results indicate that the regional biases in 20RV of temperature extremes are dominated by GEV location parameter (related to mean intensity) with relatively small contribution from GEV scale/shape parameters (related to interannual variability). CMIP6-simulated 20RV of RX1day is characterized by dry biases over the tropics and subtropical rain band areas, as in the CMIP5 models, for which biases in both GEV location and scale/shape parameters are important.

这项研究评估了参与耦合模型比对项目第6阶段（CMIP6）的全球气候模型在模拟气候变化检测和指数专家团队（ETCCDI）定义的极端气候指数方面的性能。我们使用1981-2000年期间的均方根误差，比较了CMIP6模型与HadEX3和四个再分析数据集以及CMIP5多模型集合所模拟的指标的全球气候模式。为政府间气候变化专门委员会第六次评估报告定义的41个次区域进行了区域评估。尤其是，使用广义极值（GEV）分析，分析了最暖日和最冷夜温度（TXx和TNn）的20年回报值（20RV）和日降水量的年度最大值（RX1day）的区域平均偏差。结果表明，与CMIP5模型相比，CMIP6模型通常捕获了观察到的全球和区域极端温度模式，但改进有限。高纬度地区极端寒冷地区的系统性偏见，例如冷偏见，即使幅度更大也依然存在。CMIP6模型在降水强度和频率指数方面的技能也与CMIP5模型基本相当，但发现降水强度模拟可通过减少干燥偏差而得到改善。GEV分析结果表明，极端温度在20RV中的区域偏差主要由GEV位置参数（与平均强度有关）控制，而GEV尺度/形状参数（与年际变化有关）的贡献相对较小。与CMIP5模型一样，CMIP6模拟的RX1day的20RV的特征是在热带和亚热带雨带区域上存在干偏差，对于该偏差，GEV位置和尺度/形状参数的偏差都很重要。