Effect of diversified performance metrics and climate model weighting on global and regional trend patterns of precipitation and temperature

Summary : 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 precipita - tion 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 sce - narios are evaluated against the ERA -20C reanalysis. The results indicate large agreement between three out of four met - rics. 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