| Abstrakt: |
Morocco is encountering record daily maximum temperatures, severe rainfall deficits, intense thunderstorms, droughts, and powerful wind gusts, causing significant harm to people and property. Therefore, it is crucial to understand the course of these occurrences and to determine to what extent the global climate models (GCMs) used to project climate can replicate rainfall before they can be used in downscaling or impact assessment studies. GCMs are essential tools for climate studies, but selecting the best-performing ones remains challenging. This study aims to assess the extent to which certain climate models from the Coupled Model Intercomparison Project’s 6th phase (CMIP6) reproduce the spatial and temporal variability of precipitation across Morocco between 1981 and 2014. Total monthly precipitation from the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) were used as observational references. We used six robust statistical metrics on monthly and annual scales, including relative bias, correlation coefficient, root means square error, relative error, Taylor diagram, and Kling–Gupta efficiency. The outcomes demonstrated that the ability of GCMs to simulate precipitation varied over space and time. The spatio-temporal properties of precipitation were well reproduced by all GCMs, with correlation values ranging from 0.78 to 0.87. The research also revealed that only a few models accurately captured the spatial patterns of the detected trends. According to the KGE metric, the GCM INM_CM5_0 is ranked first among the models with the highest KGE value (0.45), followed by GCM FGOALS_f3_L with a value of around 0.41. The study results can be applied to climate projections using CMIP6 under different IPCC scenarios. |
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