Multi‐model climate experiments carried out as part of different phases of the Coupled Model Intercomparison Project (CMIP) are crucial to evaluate past and future climate change. The reliability of models' simulations is often gauged by their ability to reproduce the historical climate across many time scales. This study compares the global mean surface air temperature from 29 CMIP6 models with observations from three datasets. We examine (1) warming and cooling rates in five subperiods from 1880 to 2014, (2) autocorrelation and long‐term persistence, (3) models' performance based on probabilistic and entropy metrics, and (4) the distributional shape of temperature. All models simulate the observed long‐term warming trend from 1880 to 2014. The late twentieth century warming (1975–2014) and the hiatus (1942–1975) are replicated by most models. The post‐1998 warming is overestimated in 90% of the simulations. Only six out of 29 models reproduce the observed long‐term persistence. All models show differences in distributional shape when compared with observations. Varying performance across metrics reveals the challenge to determine the “best” model. Thus, we argue that models should be selected, based on case‐specific metrics, depending on the intended use. Metrics proposed here facilitate a comprehensive assessment for various applications.

中文翻译：

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CMIP6历史全球平均温度模拟的稳健性：趋势，长期持久性，自相关和分布形状

在耦合模型比较项目（CMIP）的不同阶段中进行的多模型气候实验对于评估过去和未来的气候变化至关重要。模型仿真的可靠性通常通过其在多个时间范围内再现历史气候的能力来评估。这项研究将来自29个CMIP6模型的全球平均地面气温与来自三个数据集的观测结果进行了比较。我们研究了（1）从1880年到2014年的五个子时期的升温和降温率，（2）自相关和长期持久性，（3）基于概率和熵度量的模型性能，以及（4）温度的分布形状。所有模型都模拟了从1880年到2014年观测到的长期变暖趋势。大多数模型都复制了20世纪后期的变暖（1975–2014）和中断（1942–1975）。90％的模拟都高估了1998年以后的变暖。29个模型中只有6个可以重现观察到的长期持久性。与观察结果相比，所有模型均显示出分布形状的差异。跨指标的性能变化揭示了确定“最佳”模型的挑战。因此，我们认为应根据具体情况，根据具体情况选择模型。此处提出的度量标准有助于对各种应用程序进行全面评估。跨指标的性能变化揭示了确定“最佳”模型的挑战。因此，我们认为应根据具体情况，根据具体情况选择模型。此处提出的度量标准有助于对各种应用程序进行全面评估。跨指标的性能变化揭示了确定“最佳”模型的挑战。因此，我们认为应根据具体情况，根据具体情况选择模型。此处提出的度量标准有助于对各种应用程序进行全面评估。