Seasonal-scale drought forecasting skill is derived from the initial hydrologic state and seasonal scale climate forecasts. Therefore, to better estimate the initial hydrologic state satellite-based soil moisture and terrestrial water storage (TWS) retrievals are assimilated with LIS land surface models (LSMs), i.e., NASA’s Catchment and the Noah Multi-Physics (MP) model, which incorporate prognostic water table schemes. Seasonal scale climate forecasts are taken from downscaled and bias-corrected versions of NASA’s Goddard Earth Observing System Model, version 5 (GEOS-5), and NOAA’s Climate Forecast System, version 2 (CFSv2) forecast datasets. Long-term historical reconstructions of drought conditions are generated by driving the LSMs for 30+ years with NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) daily precipitation dataset. The LSMs’ total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which provides an additional means of validating hydrological drought events against available long-term in situ streamflow data. For this presentation, drought forecasts of soil moisture, ET, TWS and streamflow are evaluated at different lead times out to 6 months for East Africa, which encompasses some of the most food and water insecure regions.