Snow cover is highly sensitive to changes in temperature and precipitation, making it an important indicator of climate variability and climate change. It also strongly influences hydrological processes, for example, groundwater recharge, runoff formation, and flood risk during snowmelt. Long-term, spatially detailed information on snow depth and snow cover duration is therefore needed for climate monitoring, hydrological applications, and impact assessments.<br />Here, we present SNOWRAS, a new gridded snow depth dataset for Germany covering extended winter seasons from 1950/51 to 2025/26. The dataset is based on daily snow depth observations from the extensive monitoring network of the Deutscher Wetterdienst (DWD), complemented by measurements from partner networks in Germany and neighboring countries. After quality control and data cleaning, the station observations were interpolated to a regular 1 × 1 km grid using an optimal interpolation scheme.<br />Based on this dataset, we derived and analyzed several snow-climatological indicators, including seasonal mean and maximum snow depth, the number of snow days, and the timing of the first and last days with seasonal snow cover. Across Germany, most indicators show a statistically significant decline over the past 75 years. The magnitude of these changes varies considerably with elevation: the largest decreases in snow depth and snow cover duration are found in lowland and mid-elevation regions, while these trends are generally weaker at higher elevations and are partly not statistically significant. SNOWRAS provides a spatially consistent, high-resolution dataset for monitoring snow conditions and investigating long-term changes in Germany’s snow climate. Together with complementary information, such as snow water equivalent, the dataset can also support hydrological modeling, assessments of water availability, and analyses of hydrological risks.