DeepCut: adaptive neural network thresholds for precipitation phase partitioning

Accurately predicting precipitation phase is critical for hydrology, weather forecasting, and climate applications, yet fixed temperature thresholds perform inconsistently across regions. We present DeepCut, a neural network that learns adaptive, context-dependent (observation-conditional) temperature thresholds conditioned on location, climate and meteorological state and applies them in a differentiable classification stage. Unlike conventional black-box models, DeepCut offers transparency by explicitly predicting observation-conditional transition cutoffs where rain transitions to snow, combining high accuracy with physical interpretability. Using 16. 8 million surface reports from 11 626 stations across the Northern Hemisphere over the training period 1978–2005, our model reaches the accuracy rate of 93. 34% for the independent test period 2006–2007, significantly higher than existing models. More importantly, the predicted thresholds (model outputs) exhibit spatially coherent patterns when aggregated over time and stations, generally consistent with known physical processes. Adding explicit temporal encodings (month d ^−1) provides little incremental benefit beyond meteorological predictors in our experiments; we therefore treat them as optional. We nevertheless observe slowly varying long-term signals over the study period, which are reflected in the data and can be captured through the model’s covariates and regional context. Finally, we have considered atmospheric profiles in addition to near-surface temperature variables that have typically been included in previous studies. Although these profile predictors do not increase overall predictive accuracy in our experiments, their demonstrated relationships with the thresholds are promising in explaining the phase transition process and worth further investigation in future research.