Process-aware mixture modelling of precipitation-event distributions using event-type clustering

Abstract. Accurate modelling of event-level precipitation distributions is critical for hydrological modelling, flood risk assessment, and climate studies, yet selecting suitable parametric families remains challenging. We propose a process-aware approach that first separates precipitation events into types and then constructs an overall mixture from the resulting type-specific distributions. Using 10-min precipitation records from two climatically contrasting Austrian weather stations (Graz Universität and Dornbirn, 2010–2022), we compare partitioning (k-means) with outcome-guided, model-based clustering implemented as finite mixtures of Gamma regressions (clusterwise distributional regression). Events are characterised by severity, magnitude, duration, intensity, time-to-peak, and lightning occurrence. While partitioning yields interpretable event groups, the outcome-guided approach provides substantially better distributional fit–reducing Kullback-Leibler divergence by 66–73 % relative to a single-Gamma baseline–and improves visual diagnostics. Single-distribution baselines underrepresent process heterogeneity in precipitation; process-aware clusterwise mixtures that account for event-level structure address this gap. The framework is distribution-agnostic and portable, relying on information available at the event scale and lightning data, and can support applications such as hydrological modelling and stochastic weather generation.