Technical Abstract: NOAA's Climate Prediction Center issues seasonal climate forecasts predicting total precipitation and average air temperature for three-month periods out to a year in advance. The utility of seasonal forecasts for agricultural applications depends on several forecast characteristics, including dependability. This analysis addresses a practical question: whether enough dependability survives our spatial and temporal downscaling methodology to provide potentially useful information at the field and monthly scale to use in crop models to predict climate forecast impacts. Average correlations were computed between the sign of departures of 3-month forecast division values versus 1-month station values of precipitation and temperature over a 10 year study period for 96 stations in six regions of the U.S. Average correlations over all 96 stations and months were 0.76 for average temperature, and 0.66 for total precipitation. These correlation factors are multiplied to reliability values computed for the large scale forecasts to produce estimates of the net reliability for downscaled forecasts at locations. The resulting net reliability is dependent on region and forecast variable, with the forecasts for above average temperature emerging as worthy of consideration over 76% of the contiguous U.S. Conversely, forecasts for cooler than average temperature do not retain sufficient net dependability after downscaling to be an attractive option anywhere in the contiguous U.S. Forecasts for wetter or drier than average conditions retained sufficient net dependability to encourage further development over only about 10% of the contiguous U.S., in portions of Florida, Texas, southwest New Mexico, Arizona, California, Oregon, Washington, Idaho, and Montana.