Intercomparison of Gauge-Based, Reanalysis and Satellite Gridded Precipitation Datasets in High Mountain Asia: Insights from Observations and Discharge Data

High Mountain Asia (25–40° N, 70–100° E) plays a critical role in sustaining water resources for nearly two billion people; however, the accurate estimation of precipitation remains challenging. Numerous gridded products have been developed, yet their performance across the region remains uncertain and is often analyzed only over small areas or short periods. This study provides a comprehensive evaluation of five major gridded precipitation datasets (ERA5, HARv2, GPCC, APHRODITE, and PERSIANN-CDR) over 1983–2007 throughout the entire domain through spatial intercomparison, validation against ground stations, and assessment against observed river discharge. Results show that reanalysis products (ERA5, HARv2) better capture spatial precipitation patterns, particularly along the Himalayas and Kunlun range, with HARv2 more accurately representing elevation-dependent gradients. Gauge-based (GPCC, APHRODITE) and satellite-derived (PERSIANN-CDR) datasets exhibit smoother fields and weaker orographic responses. In catchment-scale evaluations, reanalysis shows a superior performance, with ERA5 achieving the lowest bias, highest Kling–Gupta Efficiency, and best water-balance consistency. GPCC and PERSIANN-CDR underestimate discharge, and APHRODITE performs worst overall. No single dataset is optimal for all applications. Gauge-based datasets and PERSIANN-CDR are suitable for localized climatology in well-instrumented areas, while reanalysis products offer the best compromise between spatial realism and hydrological consistency for large-scale modelling in high-altitude regions where observations are limited.