This study utilizes the Weather Research and Forecasting model (WRF) to produce 9 km and 3 km resolution forecasts from the Global Forecast System (GFS) model for microclimatic, agricultural regions in the U.S. Northeast. These forecasts are then statistically post-processed to generate probabilistic forecasts for temperature, specific humidity, incoming solar radiation, and precipitation. A comparison of forecast skill was conducted between these post-processed forecasts, the raw WRF output, the GFS forecasts, and forecasts from the National Weather Service’s National Digital Forecast Database (NDFD). Overall, significant improvement was observed in post-processed WRF forecasts over all other methods for all regions and variables. Furthermore, 9 km post-processed WRF had the same forecast skill as 3 km post-processed WRF, rendering 3 km WRF unnecessary if observational data is available. NDFD was found to be competitive with raw WRF for temperature, so that if observational data is unavailable for post-processing, the NDFD forecast method should be selected over running high resolution ensemble WRF.