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Abstract Winter storms have widespread impacts across the contiguous United States (CONUS), resulting in travel and economic disruptions, loss of life, and damage to property and infrastructure. Snow-to-liquid ratio (SLR), the ratio of new (i.e., freshly fallen) snow to liquid precipitation equivalent, is used operationally to forecast snowfall amount during winter storms. However, no clear best prediction method for SLR has been established, and some operational SLR prediction methods were developed for specific regions, potentially limiting their utility for CONUS-wide applications. Using Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) snowfall observations, station geographic information, and fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) vertical profiles of atmospheric variables, we developed a random forest (RF)-based algorithm for predicting SLR across the CONUS. SLR forecasts and quantitative snowfall forecasts (QSFs) based on this algorithm were then derived from the NOAA High-Resolution Rapid Refresh (HRRR). The resulting RF SLR mean absolute error (MAE) was 2.92 across the CONUS compared to MAEs for current operational methods of 3.51–4.76. Improvements were also seen in QSF. Additionally, the RF outperformed operational SLR methods within individual regional snow climates. Despite these improvements, there were situations when the RF struggled, such as high (>20) and low (<5) SLR events that featured relatively cold and calm and warm and windy environments, respectively, in the eastern CONUS. The use of CoCoRaHS observations for training provides a template for improving snowfall forecasts at a CONUS-wide scale with the RF providing an algorithm for improving SLR prediction for operational applications. Significance Statement Despite improved weather forecasts in recent years, forecasting winter storms remains challenging. Using manual snowfall observations collected by trained observers across the contiguous United States (CONUS), we developed a machine learning method to predict snow-to-liquid ratio (SLR), an important variable used to forecast snowfall and related hazards during winter storms. This machine learning method outperforms existing methods used operationally by the National Weather Service (NWS) and is available for application with operational weather prediction models for improved winter storm forecasts.