A main task of climate research is to provide estimates about future climate change under global warming conditions. The main tools for this are dynamic climate models. However, different models vary quantitatively and in some aspects even qualitatively in the climate change signals they produce. In this study, this uncertainty about future climate is tackled by the evaluation of climate models in a standardized setup of multiple regions and variables based on four sophisticated metrics. Weighting models based on their performance will help to increase the confidence in climate model projections. Global and regional climate models are evaluated for 50-year trends of simulated seasonal precipitation and temperature. The results of these evaluations are compared, and their impact on probabilistic projections of precipitation and temperature when used as bases of weighting factors is analyzed. This study is performed on two spatial scales: seven globally distributed large study areas and eight sub-regions of the Mediterranean area. Altogether, over 62 global climate models with 159 transient simulations for precipitation and 119 for temperature from four emissions scenarios are evaluated against the ERA-20C reanalysis. The results indicate large agreement between three out of four metrics. The fourth one addresses a new climate model characteristic that shows no correlation to any other ranking. Overall, especially temperature shows a high agreement to the reference data set while precipitation offers better potential for weighting. Because of the differences being rather small, the metrics are better suited for performance rankings than as weighting factors. Finally, there is conformity with previous model evaluation studies: both the model performance and the implications of weighting for probabilistic climate projections strictly depend on the selected region, season and variable. Thus, none of the climate models generally outperforms all others.

中文翻译：

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多样化的性能指标和气候模型加权对全球和区域降水和温度趋势模式的影响

气候研究的一个主要任务是提供对全球变暖条件下未来气候变化的估计。为此，主要工具是动态气候模型。然而，不同的模型在它们产生的气候变化信号中在数量上有所不同，在某些方面甚至在质量上有所不同。在这项研究中，通过在基于四个复杂指标的多个区域和变量的标准化设置中评估气候模型来解决这种关于未来气候的不确定性。根据其性能对模型进行加权将有助于增加对气候模型预测的信心。评估全球和区域气候模型的 50 年模拟季节性降水和温度趋势。比较这些评估的结果，并分析了它们在用作加权因子基础时对降水和温度概率预测的影响。这项研究在两个空间尺度上进行：七个全球分布的大型研究区和地中海地区的八个子区域。总共有超过 62 个全球气候模型根据 ERA-20C 再分析评估了来自四种排放情景的 159 个降水瞬态模拟和 119 个温度瞬态模拟。结果表明四个指标中的三个指标之间存在很大的一致性。第四个解决了一个新的气候模型特征，该特征与任何其他排名没有相关性。总体而言，尤其是温度与参考数据集高度一致，而降水提供了更好的加权潜力。因为差异比较小，与作为权重因素相比，这些指标更适合用于绩效排名。最后，与之前的模型评估研究一致：模型性能和对概率气候预测加权的影响都严格取决于所选区域、季节和变量。因此，没有一种气候模型通常优于其他所有气候模型。