Improving Seasonal Precipitation Forecasts in the Western United States Through Statistical Downscaling

Seasonal precipitation forecasts are essential tools for water management, especially in drought-prone areas like the Western United States. While global forecasting systems can predict precipitation months to years in advance, these forecasts are typically produced at a coarse spatial resolution due to high computational costs – especially when multiple forecast iterations, or ensemble members, are included. This coarse resolution limits their ability to capture localized weather features, such as those shaped by the complex terrain of the Intermountain West. Here, analog statistical downscaling is demonstrated as an effective approach to enhance the spatial resolution of operational seasonal forecasts provided by the North American Multi-Model Ensemble within this challenging region. Additionally, we find that downscaling individual ensemble members – rather than downscaling the ensemble mean – results in greater forecast skill. These findings demonstrate a low-cost method to improve seasonal forecasts, providing a valuable framework for enhancing coarse resolution products through downscaling.