Traditional methods for assessing fire danger often depend on meteorological forecasts, which have reduced reliability after ∼10 d. Recent studies have demonstrated long lead-time correlations between pre-fire-season hydrological variables such as soil moisture and later fire occurrence or area burned, yet the potential value of these relationships for operational forecasting has not been studied. Here, we use soil moisture data refined by remote sensing observations of terrestrial water storage from NASA's Gravity Recovery and Climate Experiment (GRACE) mission and vapor pressure deficit from NASA's Atmospheric Infrared Sounder (AIRS) mission to generate monthly predictions of fire danger at scales commensurate with regional management. We test the viability of predictors within nine US geographic area coordination centers (GACCs) using regression models specific to each GACC. Results show that the model framework improves interannual wildfire-burned-area prediction relative to climatology for all GACCs. This demonstrates the importance of hydrological information to extend operational forecast ability into the months preceding wildfire activity.