Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

使用气候变化检测和指数专家团队定义的降水指数，对耦合模型比对项目第6阶段（CMIP6）模型的模拟历史降水进行评估。根据来自CPC统一量表的连续美国（CONUS）七个地理区域的降水量分析得出的相对指数对模型指数进行了评估。评估的地区与美国最近的《国家气候评估报告》中的地区匹配。为了估计观测的不确定性，针对其他三个观测数据集（HadEx2，Livneh和PRISM）的降水指数根据CPC分析进行了评估。在大多数测试的CMIP6模型中，中西部和中部的极端降水强度都被高估了，而中部大平原（CGP）地区却被低估了。大多数CMIP6模型高估了湿法持续时间的平均值和变异性，而低估了整个CONUS的干法持续时间的平均值和变异性。大多数指数的年际变化偏差与相应的均值偏差具有相似的模式。在湿拼期间，CMIP6模型偏差的中位数和四分位数模型分布明显小于CMIP5模型偏差的中位数和四分位数模型分布。CMIP6（CMIP6-MMM）和CMIP5（CMIP5-MMM）的多模型中位数在气候和变异性方面具有相似的偏差，但CMIP6-MMM中的偏差往往较小。根据索引，大多数指数的年际变化偏差与相应的均值偏差具有相似的模式。在湿拼期间，CMIP6模型偏差的中位数和四分位数模型分布明显小于CMIP5模型偏差的中位数和四分位数模型分布。CMIP6（CMIP6-MMM）和CMIP5（CMIP5-MMM）的多模型中位数在气候和变异性方面具有相似的偏差，但CMIP6-MMM中的偏差往往较小。根据索引，大多数指数的年际变化偏差与相应的均值偏差具有相似的模式。在湿拼期间，CMIP6模型偏差的中位数和四分位数模型分布明显小于CMIP5模型偏差的中位数和四分位数模型分布。CMIP6（CMIP6-MMM）和CMIP5（CMIP5-MMM）的多模型中位数在气候和变异性方面具有相似的偏差，但CMIP6-MMM中的偏差往往较小。根据索引，CMIP6-MMM的CONUS东半部部分地区的极端降水稍好一些，否则，气候和变异性的偏差与CMIP5-MMM相似。在某些情况下，CMIP6-MMM的性能要优于单个模型甚至是观察数据集。大多数索引的观测数据集之间的差异可与CMIP6四分位数模型的分布相媲美。表现较好的观测和模型数据集在CONUS的不同部分有所不同。