The relative value of dynamical vs. statistical downscaling of Climate Forecast System (CFS) forecasts for seasonal hydrologic forecasting is assessed. The dynamically downscaled retrospective climate forecasts were produced by the MRED (“Multi-RCM Ensemble Downscaling of NCEP CFS Seasonal Forecasts”) project. In MRED, multiple Regional Climate Models (RCMs) were used to downscale CFS wintertime seasonal forecast from original spatial resolution of 2.5 degree to 0.375 degree (dynamical downscaling). Each RCM's output is comprised of 10-15 ensemble members for the 27 year period 1982-2008 for the forecast period Dec. 1-Apr. 30, with forecast initialization dates at 0000 UTC Nov. 11-15, 21-25, 29-Dec. 3. The spatial domain is the Conterminous United States (CONUS). In this work we assess the value of the dynamical MRED downscaling in comparison with a much simpler bias correction and spatial downscaling (BCSD) (statistical downscaling); specifically in terms of the resultant seasonal forecast skill of hydrologic variables such as Runoff (RO), Snow Water Equivalent (SWE) and Soil Moisture (SM). The bias correction uses a probability mapping approach, applied both to dynamically downscaled and the CFS (at its native resolution), precipitation, Tmax and Tmin forecasts corrected to the statistics of a gridded observation data set. Both sets of forecasts were then spatially downscaled from their original spatial resolutions (0.375 degree and 2.5 degree respectively) to the spatial resolution of the Variable Infiltration Capacity (VIC) hydrologic model (0.125 degree) using a resampling approach. We conducted two separate experiments with both dynamically and statistically downscaled forecasts to generate reforecasts of RO, SWE and SM; with the initial hydrologic state (IHS) derived by forcing the VIC hydrology model with observed forcings over a multi-year spinup period. A “synthetic truth” data set of RO, SWE and SM was generated as a simulation using the gridded observation data set to force the VIC model. We estimated Root Mean Square Error (RMSE)-based skill scores for each experiment for lead times (1-5 months) by comparing forecasts of monthly values of RO, SWE, and SM at each lead times with their respective values obtained from the “synthetic truth” data set. Based on the RMSE score we assessed the value of dynamically vs. statistically downscaled CFS forecasts and identified the regions across CONUS and lead times when dynamical downscaling of CFS forecasts results in to some or no improvement of hydrological forecast skill relative to statistically downscaled forecasts.