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Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.

Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill. Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill.