Accurate prediction of precipitation in complex terrain is difficult but critical in reiognal climate simulations to identify water resources. These simulations also become an indispensable part of efforts to understand the hydrologic cycle at regional scales. Cloud microphysical processes are some of the key physical processes to be parameterized in numerical models employed in such regional climate studies. We explore sensitivity of assumed drop size distributions and mean diameters for various hydrometeor types in improving precipitation prediction by the Regional Atmospheric Modeling System (RAMS). The model is initialized with the 2.5 deg x 2.5 deg reanalysis data from the National Center for Environmental Prediction (NCEP). We perform both winter and summer three dimensional simulations to determine microphysical parameters appropriate for the seasons in complex terrain of the Rio Grande Basin of Northern New Mexico. Simulated precipitation is validated using surface observations obtained from both the Natural Resources Conservation Service's SNOTEL and the National Oceanic and Atmospheric Administration's COOP station data. Previous two dimensional simulations suggest that greater precipitation variability can be simulated by variations in the afore-mentioned microphysical parameters within RAMS. This sensitivity is further pronounced in winter storms in which ice-phase microphysics are dominant and are poorly parameterized