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Two high-resolution (4 km) regional climate simulations over a 10-yr period are conducted to study the changes in wintertime precipitation distribution across mountain ranges in the interior western United States (IWUS) in a warming climate. One simulation represents the current climate, and another represents an ~2050 climate using a pseudo–global warming approach. The climate perturbations are derived from the ensemble mean of 15 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). These simulations provide an estimate of average changes in wintertime orographic precipitation enhancement and finescale distribution across mountain ranges. The variability in these changes among CMIP5 models is quantified using statistical downscaling relations between orographic precipitation distribution and upstream conditions, developed in Part I. The CMIP5 guidance indicates a robust warming signal (~2 K) over the IWUS by ~2050 but minor changes in relative humidity and cloud-base height. The IWUS simulations reveal a widespread increase in precipitation on account of higher precipitation rates during winter storms in this warmer climate. This precipitation increase is most significant over the mountains rather than on the surrounding plains. The increase in precipitation rate is largely due to an increase in low-level cross-mountain moisture transport. The application of the statistical relations indicates that individual CMIP5 models disagree about the magnitude and distribution of orographic precipitation change in the IWUS, although most agree with the ensemble-mean-predicted orographic precipitation increase.

Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.