The Weather Research and Forecasting model (WRF) is used to downscale interim ECMWF Re-Analysis (ERA-Interim) data for the climate over Europe for the period 1990–95 with grid spacing of 0.448 for 12 combinations of physical parameterizations. Two longwave radiation schemes, two land surface models (LSMs), two microphysics schemes, and two planetary boundary layer (PBL) schemes have been investigated while the remaining physics schemes were unchanged. WRF simulations are compared with Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) observations gridded dataset (E-OBS) for surface air temperatures (T2), precipitation, and mean sea level pressure (MSLP) in eight subregions within the model domain to assess the performance of the different parameterizations on widely varying regional climates. This work shows that T2 is modeled well byWRF with high correlation coefficients (0.8 , R , 0.95) and biases less than 48C. T2 shows greatest sensitivity to land surface models, some sensitivity to longwave radiation schemes, and less sensitivity to microphysics and PBL schemes. Precipitation is not well modeled by WRF with low correlation coefficients (0.1 , R , 0.3) and high root-mean-square differences (RMSDs; 8–9 mm day21). Precipitation shows sensitivity to LSMs in summer. No significant bias has been observed in theMSLP modeled byWRF. Correlation coefficients are typically in the range 0.7,R,0.8 whileRMSDs are in the range 6–10 hPa. MSLP output is sensitive to longwave radiation scheme in summer but is relatively insensitive to either microphysics or the choice of LSM. The optimum combination of parameterizations for all three state variables examined is strongly dependent on subregion and demonstrates the need to carefully select parameterization combinations when attempting to use WRF as a regional climate model.