The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a "good model". This study examines the behavior of six indicators, considering spatial correlation, distribution mean, variance, and shape. A new multi-component measure was selected based on these criteria to assess the performance of 48 CMIP6 models in reproducing the annual seasonal cycle of precipitation, temperature, and teleconnection patterns in Central America. The top six models were determined using multi-criteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

翻译：气候模型评估是气候研究中的关键步骤，其旨在量化模型输出与参考数据的相似程度，以识别在复制特定气候变量方面具有优越能力的模型。显然，评价指标的选择会显著影响结果，这凸显了选取能够恰当反映"优质模型"特征的指标的重要性。本研究考察了六种指标的表现，综合考虑了空间相关性、分布均值、方差和形态等维度。基于这些标准，我们选取了一种新型多分量测量方法，用于评估48个CMIP6模型在再现中美洲地区降水、温度年际季节循环及遥相关型方面的性能。通过多准则方法确定了表现最佳的六个模型，研究发现即使最优模型在该区域复制某一派生气候变量的表现仍不理想。本文提出的测量方法与筛选机制可提升基于气候模型的气候学研究的精度。