The fifth phase of the Coupled Model Inter-Comparison Project (CMIP5) is frequently used to force regional climate models for dynamic downscaling and projections, which decision makers in turn use for future plans in different sectors. It is, therefore, highly important to assess their performance in order to use them as reliable tools. A weather-type approach for the evaluation of the performance of CMIP5 models is employed in this study, with the objective of providing insight into model errors under a set of distinct synoptic conditions and circulation types associated with the rainy season over Mexico and Central America. The Self-Organizing Maps algorithm is used to identify the main weather regimes (constructed from sea level pressure, specific humidity, and low-level winds at a daily time-scale), which are then evaluated against reanalysis. The results show that model performance depends on the weather type in all of the variables except for sea level pressure, which confirms the usefulness of this approach. The models simulate better the humidity patterns that show weak deviations from the climatological norm. In addition, the wind pattern representing the Caribbean Low Level Jet is well reproduced by all the models. The results show the capacity of this methodology for determining the extent to which climate models represent the main circulation patterns that characterize the climate and local weather in Mexico.
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