The paper explores a strategy for long-range hydrologic forecasting in the eastern United States by using ensemble climate model forecasts as input to a macroscale hydrologic model. The National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) produces monthly ensemble climate forecasts, which are bias-corrected and downscaled to a 1/8° horizontal resolution. These forecasts are then disaggregated to a daily time step for input into the Variable Infiltration Capacity (VIC) hydrologic model. The bias correction involves evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and extracting the associated variable values from the observed climatology. The monthly meteorological forecasts are interpolated to the finer hydrologic model scale, and a daily signal is imposed through resampling of the historic record. The hydrologic forecasts 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. The results show that the hydrologic forecasting strategy successfully translates climate forecast signals into hydrologic variables of interest for water management. The study highlights the importance of bias correction and downscaling in translating climate forecasts to hydrologic variables, and it demonstrates the potential of this approach for long-range hydrologic forecasting.The paper explores a strategy for long-range hydrologic forecasting in the eastern United States by using ensemble climate model forecasts as input to a macroscale hydrologic model. The National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) produces monthly ensemble climate forecasts, which are bias-corrected and downscaled to a 1/8° horizontal resolution. These forecasts are then disaggregated to a daily time step for input into the Variable Infiltration Capacity (VIC) hydrologic model. The bias correction involves evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and extracting the associated variable values from the observed climatology. The monthly meteorological forecasts are interpolated to the finer hydrologic model scale, and a daily signal is imposed through resampling of the historic record. The hydrologic forecasts 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. The results show that the hydrologic forecasting strategy successfully translates climate forecast signals into hydrologic variables of interest for water management. The study highlights the importance of bias correction and downscaling in translating climate forecasts to hydrologic variables, and it demonstrates the potential of this approach for long-range hydrologic forecasting.