<p>Many Numerical Weather Prediction (NWP) models provide the parameter total snow depth as a Direct Model Output (DMO) surface variable. In mountain regions, however, the orographic flow modification significantly influences precipitation formation and preferential settling, leading to large model biases if DMO is directly compared to fresh snow point observations. Avalanche risk forecasts in turn require calibrated deterministic and probabilistic fresh snow forecasts, as the amount of fresh snow constitutes a crucial driver of avalanche risk.</p><p>In this study, MOSMIX-SNOW, a Model Output Statistics (MOS) product based on multiple linear regression is developed. Ground-based observations and operational forecast data of the two deterministic global NWP models ICON and ECMWF form the basis of the MOS system. MOSMIX-SNOW offers point forecasts for 20 deterministic as well as probabilistic forecast variables like the amount of fresh snow within 24h, the probability of more than 30cm of fresh snow within 24h and some basic variables like 2m temperature and dew point. The unique characteristic of MOSMIX-SNOW is the large number of observation-based, model-based and empirical predictors, which exceeds 200. Furthermore, a long historical data period of 9 years is applied for training of the MOS system. Thus, local orographic effects and large scale flow patterns are implicitly included in the MOS equations by a location and lead time specific choice of predictors. To avoid unrealistic jumps in the forecast, persistence predictors, which represent the forecast value of the previous forecast hour, are included in the MOS system. All forecasts feature a maximum lead time of +48h, have an hourly forecast resolution as well as update cycle and are available for about 15 mountain locations in the Bavarian Alps between 1100m and 2400m above sea level.</p><p>The verification analysis of the winter season 2018/19 shows that MOSMIX-SNOW forecasts offer a significantly higher forecast reliability than the raw ensemble of the regional NWP model COSMO-D2-EPS. The bias of the deterministic forecast parameters is smaller for MOSMIX-SNOW, especially for heavy snowfall events. MOSMIX-SNOW turned out to be a useful tool to support the avalanche risk forecasts on a daily basis during the snowy winter of 2018/19. Furthermore, the deterministic fresh snow forecast of MOSMIX-SNOW and other meteorological parameters like 2m-temperature serve as input for operational snowpack simulations. Measurement related noise and snow drift in the observations, however, are identified as an important source of uncertainty and the application of noise reduction techniques like a Savitzky-Golay filter are expected to have a beneficial impact on the forecast quality. MOSMIX-SNOW will become operational by end of 2020.</p>