Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region.

This study presents a multiscale evaluation of five gridded climate products, ERA5-Land, TerraClimate, CHIRPS, CHIRTS, and PERSIANN CCS-CDR, over the semiarid topographically complex High Atlas Mountains of Morocco. Using observations from nine ground stations, performance was assessed at daily, monthly, and annual scales through correlation coefficient, RMSE, bias, and MAE metrics. Results reveal substantial spatial and temporal variability in accuracy. ERA5-Land consistently achieved the highest agreement with observations (CC = 0.92–0.99 for temperature), attributed to its multisource data assimilation framework. TerraClimate also performed well, particularly for temperature and monthly precipitation, benefiting from hybrid downscaling and elevation correction. In contrast, satellite-based products exhibited pronounced systematic biases, including precipitation overestimation (+110% for CHIRPS), underestimation (−34% for PERSIANN CCS-CDR), and significant temperature biases for CHIRTS, especially at high-altitude stations. Spatial analysis confirmed a consistent decline in performance with increasing elevation, underscoring the limitations of both reanalysis and satellite-based estimation in mountainous terrain. These findings stress the importance of bias correction and careful dataset selection for hydrological, agricultural, and climate change applications. More broadly, this work offers a transferable evaluation framework for data-scarce, topographically complex regions, providing actionable guidance for researchers, water managers, and policymakers supporting climate adaptation strategies.