Reliable avalanche forecasting is essential for protecting mountain communities and transportation corridors, but estimating avalanche occurrence from monitored meteorological and snowpack conditions remains difficult. Operational assessments often rely primarily on meteorological thresholds, although avalanche responses depend on both type-specific triggering process and the snowpack conditions. Using meteorological, pre-event snowpack, and avalanche observations collected during the 2024 and 2025 snow seasons, we analysed 37 recorded avalanche events, together with corresponding non-avalanche periods. Separate logistic-regression models were developed for dry- and wet-snow avalanches using the intensity and duration of the preceding snowfall or snowmelt process and background snow depth. Their performance was compared with otherwise identical models excluding snow depth. Under leave-one-out cross-validation, including background snow depth increased the area under the receiver operating characteristic curve from 0.72 to 0.83 for dry-snow avalanches and from 0.85 to 0.94 for wet-snow avalanches. For wet-snow avalanches, the true-positive rate increased from 0.79 to 0.92, while the false-positive rate decreased from 0.21 to 0.12. These results demonstrate that pre-event snowpack conditions provides predictive information beyond meteorological forcing alone and improves avalanche forecasting, particularly for wet-snow avalanches.