This study assesses the impact of assimilating pseudo-observations for water vapor mass derived from the Geostationary Operational Environmental Satellites GOES-16 lightning data on short-term quantitative precipitation forecasts (QPFs) over the contiguous United States (CONUS), with an emphasis given to regions characterized by an overall poor radar coverage. The GOES-16/17 provide nearly uniform and high temporal resolution total lightning observations over most of the Americas. To leverage this information for convective scale weather forecasts, a three-dimensional variational data assimilation (DA) package developed by the National Severe Storm Laboratory was employed to assimilate these data. To mimic operational settings, the Weather Research and Forecasting Model configuration used in NOAA Global Systems Laboratory’s High-Resolution Rapid Refresh Model version 4 was utilized. During the 2020 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, four experiments were run in real time during a 29-day period over CONUS and surrounding territories to assess the added value of GOES-16 lightning over conventional radar data. Overall, the lightning DA (LDA) showed benefit in improving QPFs up to 6 h, with the best improvements seen during the first 3 hours. Owing to notably larger lightning activity over the eastern CONUS, the most noticeable impacts from the LDA were seen there. Case-by-case analysis revealed that positive impacts from the LDA were seen over areas characterized by both good (eastern two thirds of CONUS) and poor radar coverage (western one third of CONUS). Highlighting the inherent difficulties in developing an optimal observation operator based on moisture for LDA applications, the GLM-based DA experiments systematically overestimated rainfall over the eastern two thirds of CONUS while underestimating it over the western one third of CONUS.