The relatively low density of weather radar networks can lead to low-altitude coverage gaps. As existing networks are evaluated for gap fillers and new networks are designed, the benefits of low-altitude coverage must be assessed quantitatively. This study takes a regression approach to modeling quantitative precipitation estimation (QPE) differences based on network density, antenna aperture, and polarimetric bias. Thousands of cases from the warm-season months of May–August 2015–17 are processed using both the specific attenuation R(A) and reflectivity–differential reflectivity R(Z, Z DR) QPE methods and are compared with Automated Surface Observing System (ASOS) rain gauge data. QPE errors are quantified on the basis of beam height, cross-radial resolution, added polarimetric bias, and observed rainfall rate. The collected data are used to construct a support vector machine regression model that is applied to the current WSR-88D network for holistic error quantification. An analysis of the effects of polarimetric bias on flash-flood rainfall rates is presented. Rainfall rates that are based on 2-yr/1-h return rates are used for a contiguous-U.S.-wide analysis of QPE errors in extreme rainfall situations. These errors are then requantified using previously proposed network design scenarios with additional radars that provide enhanced estimate capabilities. Last, a gap-filling scenario utilizing the QPE error model, flash-flood rainfall rates, population density, and potential additional WSR-88D sites is presented, exposing the highest-benefit coverage holes in augmenting the WSR-88D network (or a future network) relative to QPE performance.