Shifts in precipitation patterns due to global warming highlight the need to understand precipitation variability across diverse climatic regimes. Research on precipitation distribution, patterns, intensity, duration, and frequency over drylands is lacking but crucial for quantitative precipitation estimation and climate projection accuracy. Drylands depend solely on satellite remote sensing due to unavailability of continuous ground-based precipitation monitoring. Validating satellite-based precipitation observations is crucial to ensure accuracy before their use in weather prediction and climate models. In exploring biases in satellite retrievals for regional-scale variability over semi-arid Southwest US (West Texas), we performed comprehensive intercomparison between high spatially resolved ground-based observations (West Texas Mesonet) and satellite derived precipitation estimates (GPM-IMERG). Analyses revealed noticeable spatial gradients in precipitation from west-east and south-north. Satellite retrievals exhibited a positive relative bias of precipitation indicating overestimation, with 62.6% (37.4%) measurements showing a positive (a negative) bias, and 2.6% exhibiting extreme negative bias.