How well do RCMs simulate Portugal’s climate?

Extreme climate-related events such as droughts, floods, and heatwaves are becoming more frequent, making climate models essential tools for understanding and predicting these phenomena despite their inherent uncertainties. Continuous performance evaluation is crucial to select the most reliable models for impact assessments and to guide ongoing model improvement. This study evaluates the performance of all available EURO-CORDEX regional climate model simulations (RCMs) providing daily variables at 0.11° resolution over Portugal for the period 1971 to 2004. Three key surface variables, daily precipitation, daily minimum temperature, and daily maximum temperature, were assessed using nine mean and extreme climate indices, benchmarked against two high-resolution observational datasets, Iberia01 and E-OBS. The evaluation employed the Aras diagram to analyze model bias, variability, correlation, and total error. Results indicate that model performance varies notably by variable and index. Maximum temperature mean indices showed the highest accuracy, with total errors generally below 30%, followed by minimum temperature mean indices with errors under 40%. In contrast, precipitation indices exhibited the greatest uncertainties, with total errors exceeding 50% for extreme precipitation indices such as annual maxima and consecutive dry days across all models. Mean climate indices were better captured than extremes, with over 85% of model simulations achieving total errors below 50% for mean temperature indices. Spatial analysis of total error was performed at the individual grid-cell level, revealing localized strengths and weaknesses across Portugal and highlighting regional variations in model skill. These findings demonstrate that while EURO-CORDEX RCMs reliably capture temperature-based indices, challenges remain in simulating precipitation extremes. The comprehensive evaluation across multiple indices provides valuable guidance for model selection and future improvements in regional climate and impact assessments for Portugal.