Accurate estimation of precipitation is critical for hydrology, study of the Earth system, and various water-related applications. This study investigates the performance of the Integrated Multi-SatellitE Retrievals for GPM version 6 (IMERG V6) and its components as a function of surface temperature and condition over the CONUS. IMERG is composed of precipitation estimates from Passive Microwave (PMW) and Infrared (IR) sensors. Previous studies have reported large uncertainties for PMW precipitation estimates over snow and ice surface that can lead to large errors in determining surface emissivity. In such situations IMERG has used IR-based precipitation estimates within 60S/N, where IR images from geostationary satellites provide frequent sampling. Precipitation estimates from IR and various PMW sensors were compared against hourly Stage IV precipitation estimates using quantitative and binary skills scores within various surface air temperature bins and separately over surfaces with and without snow and ice cover. The results suggest: (1) PMW and IR precipitation estimates have higher skills in capturing intense precipitation, regardless of temperature and surface cover, (2) PMW indicates higher skill than IR precipitation estimates in detection of precipitation occurrence and estimation of precipitation rate over surfaces with and without snow and ice (3) spatial distribution of a case study precipitation event shows that patterns of PMW and IR precipitation rates and their combination can largely be different from the reference Stage IV precipitation data, even after monthly bias corrections applied in the IMERG Final product using rainfall gauges.