Historical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial-condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root-mean-square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature-related extremes over Australia.

中文翻译：

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在 CMIP5 和 CMIP6 模型的历史模拟中评估澳大利亚的极端温度

参与耦合模型比对项目 (CMIP6) 第六阶段的模型的历史模拟评估了澳大利亚 10 多个地区在模拟极端温度方面的性能，其中三个具有初始条件大集合 (LE) 的模型用于估计内部变异的影响。基于澳大利亚可用水项目（AWAP）和伯克利地表温度（BEST）两个观测数据集，我们首先分析了模型模拟日最高和最低温度（TX和TN）概率分布的能力，然后由气候变化检测和指数专家组 (ETCCDI) 定义的极端指数的空间模式和时间变化。总体而言，CMIP6 模型与 CMIP5 相当，CMIP6 中显示了适度的改进。与 CMIP5 相比，CMIP6 集合对于一些寒冷的极端情况往往具有更窄的四分位数模型范围，并且对于大多数指数的时间趋势集合范围更窄。在东南部、热带和南部地区，两个 CMIP 集合在模拟极端温度方面普遍存在较大的缺陷。我们还确认内部可变性会影响极端情况的趋势，并且在表示 CMIP6 中不同 LE 之间不可减少的可变性方面存在不确定性。此外，基于 Perkins 技能得分 (PSS) 和三个 LE 的均方根误差 (RMSE) 的评估与极端温度的趋势范围没有直接关联。