  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.
