Precipitation is the most important component of a mixture of hydrologic variables and is critical to the study of water and energy cycle. Global precipitation measurements are currently available through a limited number (5-8 per day) of passive microwave (MW) rain observations augmented by less definitive but frequent (1/2-hourly) PM-calibrated Infrared (IR) rain estimates. In this research we study the propagation of precipitation retrieval uncertainty in the simulation of hydrologic variables and fluxes (soil moisture, runoff, latent heat) for different satellite retrievals, and varying resolution, and vegetation cover. We explore two satellite rain retrievals: one based on IR-only data and a second based on combined PM and IR rain product; and two spatial grid resolutions: 0.5 and 1.0 deg. This investigation is facilitated by NCAR’s offline Community Land Model (CLM) forced with in situ met data from Oklahoma Mesonet and high-resolution (0.1 deg/hourly) rain gauge-calibrated WSR-88D radar based precipitation fields. In turn, radar rainfall is replaced by the two satellite rain estimates at coarser resolution (0.5 & 1 deg) to determine their impact on model predictions. A fundamental assumption made in this study is that CLM can adequately represent the physical land surface processes.