Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

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Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.