Evaluation of Monthly Precipitation Products in Southern Ecuador Across Basins, Climate Regimes, and Seasons Under Complex Terrain

High-resolution precipitation products are crucial for accurately capturing the spatio-temporal variability of precipitation, particularly in data-scarce regions, to support effective hydrometeorological studies and water management. Ecuador, a tropical Andean country with complex terrain and diverse climate regimes, poses significant challenges for accurate precipitation estimation. Despite known regional variability in product performance, validation efforts remain limited at basin scale. This study evaluates the performance of seven state-of-the-art precipitation products (CHIRPS V2.0, CMORPH-CRT V1.0, ERA5-Land, IMERG-FR V06/V07, MSWEP V2.2/V2.8, MSWX-Past, and PERSIANN CCS-CDR) over two climatically contrasting Andean basins in southern Ecuador that represent the country’s main precipitation regimes. A point-to-pixel comparison was conducted using monthly data from 23 rain gauges for the period 2003–2015. Three performance metrics were used to assess the quality of the precipitation products: relative mean absolute error (RMAE), correlation coefficient (r), and Kling–Gupta efficiency (KGE). The evaluation was also conducted separately for the wet and dry seasons. Results show that MSWEP V2.2, IMERG-FR V07, MSWX-Past, and CHIRPS V2.0 performed best overall. Most products yielded better estimates in the western (Pacific) basin than in the eastern (Amazon) basin, with performance largely influenced by regional precipitation regimes. Most datasets provided better estimates during the wet season, except in the Amazon regime, where dry season estimates were more accurate. Several products captured the general climatology of the precipitation regimes; however, none adequately represented intra-annual variability in the Amazon. In addition, we found that the latest product versions do not always outperform the older ones. These findings underscore the importance of evaluating precipitation datasets at basin scale in complex terrain and diverse climatic settings. The spatial heterogeneity in performance highlights the need for context-specific dataset selection in the tropical Andes.