Incorporating relative humidity in precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US

• Bivariate rain-snow partitioning reduces model bias for key hydrology metrics. • Snow phenology bias increases due to meteorological uncertainty and melt dynamics. • Differential snow and streamflow bias changes affect snowmelt-runoff interpretation. • Elevation and meteorology partly explain spatial variability in model bias changes While the importance of bivariate precipitation phase partitioning—that incorporates both surface air temperature and relative humidity—has been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50 % reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30 % to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75–88 % of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics—both of which cannot be resolved by changing the precipitation partitioning—become important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias—at least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.