Enhancing Weeks 1-2 Forecasts of 2-m Temperature in the Sierra Nevada, California through Analog Ensemble post-processing

California relies on Sierra Nevada spring snowmelt for 60% of its water, supporting 23 million residents. Forecasting the Sierra Nevada snowmelt is a critical component of producing water supply forecasts for water managers, which is undertaken by the California Department of Water Resources (DWR) in of state mandated Bulletin-120 forecasts. Accurate forecast the snowmelt relies on subseasonal 2-meter temperature (T2m) forecasts, which improve snowmelt-driven water storage and streamflow predictions, particularly in higher elevations that contribute the largest surface water input, snowpack depth, and runoff efficiency. Current systems like the California-Nevada River Forecast Center's (CNRFC) Hydrologic Ensemble Forecast Service (HEFS) have identified T2m forecasts as a key uncertainty source. However, research on fine-resolution subseasonal temperature forecasting in complex terrain is limited. This study uses the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset as ground truth and National Oceanic and Atmospheric Administration (NOAA) Global Ensemble Forecast System (GEFS) reforecasts to apply Analog Ensemble (AnEn) post-processing, producing high-resolution (4-km) daily T2m forecasts for the Sierra Nevada. We find that during the spring snowmelt season (April–July), AnEn reduces T2m forecast root-mean-squared error by 1°C (60% for 1-day leads, 20% for 15-day leads), increases correlation by ~11%, and extends skill by an additional week beyond dynamical benchmarks. Improvements are more pronounced at higher elevations (e.g., 3000–3500 m), with root-mean-squared error reduced by 4°C, correlation rising from 0.1 to 0.9, and skill extended by over two weeks. By enhancing T2m accuracy for Bulletin-120 and CNRFC-HEFS systems, AnEn can boost the precision of snowmelt and streamflow predictions, supporting improved water resource management in a changing climate. Furthermore, we expand the application of AnEn to enhance subseasonal-to-seasonal forecasts of precipitation and T2m over the western U.S., providing valuable insights for widespread water resource and disaster management.