Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12-km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions: First, how well are the 3- and 24-hr precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the NCEP/EMC 4-km Stage IV and PRISM data on 3-hr and 24-hr timescales, respectively. Our analysis suggests that the 12-km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3- and 24-hr precipitation maximum over the CONUS, in contrast to ERA5 which generally underestimates these quantities mainly over eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3- and 24-hr annual and seasonal maximum precipitation. WRF exhibits seasonally-dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year-round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics, but is susceptible to common regional climate model biases. Consequently, if used for operational purposes, we suggest moderate bias-correction be applied to the dynamically downscaled product.