This study presents a long-range hydrologic forecasting strategy that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts for water management. Monthly ensemble climate model forecasts from the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity (VIC) hydrologic model. Bias correction is achieved by evaluating GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. The resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin are evaluated during the southeastern United States drought from May to August 2000 and for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño-condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. The hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management. The study demonstrates that the downscaling procedure successfully transfers the climate forecast signals to the hydrologic variables. The results show that the hydrologic model, which performs a non-linear transformation of temperature, precipitation, and other inputs to streamflow, was able to retrieve the observed streamflow climatology from the downscaled, GSM-scale observed precipitation and temperature climatology. The study also highlights the importance of bias correction and downscaling in improving the accuracy of hydrologic forecasts. The results indicate that the hydrologic forecasts are influenced by both the climate model forecast signal and the persistence of antecedent hydrologic states. The study concludes that the downscaling procedure is effective in translating climate forecasts to hydrologic variables for water management.This study presents a long-range hydrologic forecasting strategy that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts for water management. Monthly ensemble climate model forecasts from the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity (VIC) hydrologic model. Bias correction is achieved by evaluating GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. The resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin are evaluated during the southeastern United States drought from May to August 2000 and for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño-condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. The hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management. The study demonstrates that the downscaling procedure successfully transfers the climate forecast signals to the hydrologic variables. The results show that the hydrologic model, which performs a non-linear transformation of temperature, precipitation, and other inputs to streamflow, was able to retrieve the observed streamflow climatology from the downscaled, GSM-scale observed precipitation and temperature climatology. The study also highlights the importance of bias correction and downscaling in improving the accuracy of hydrologic forecasts. The results indicate that the hydrologic forecasts are influenced by both the climate model forecast signal and the persistence of antecedent hydrologic states. The study concludes that the downscaling procedure is effective in translating climate forecasts to hydrologic variables for water management.