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The evaluation of climate models is a crucial step in climate studies. Itconsists of quantifying the resemblance of model outputs to reference data toidentify models with superior capacity to replicate specific climate variables.Clearly, the choice of the evaluation indicator significantly impacts theresults, underscoring the importance of selecting an indicator that properlycaptures the characteristics of a "good model". This study examines thebehavior of six indicators, considering spatial correlation, distribution mean,variance, and shape. A new multi-component measure was selected based on thesecriteria to assess the performance of 48 CMIP6 models in reproducing the annualseasonal cycle of precipitation, temperature, and teleconnection patterns inCentral America. The top six models were determined using multi-criteriamethods. It was found that even the best model reproduces one derived climaticvariable poorly in this region. The proposed measure and selection method cancontribute to enhancing the accuracy of climatological research based onclimate models.

Comparison of indicators to evaluate the performance of climate models