A lightweight framework for characterising extreme precipitation events in climate ensembles

2026-03-18

AI Summary

This study addresses the efficient estimation of exceedance probabilities for multi-location extreme precipitation events within climate ensembles and the characterization of their spatiotemporal dependence. Methodologically, the multivariate problem is reduced to univariate modeling via spatial order statistics. Within a peaks-over-threshold framework, marginal excesses are modeled using the generalized Pareto distribution, with its parameters allowed to vary smoothly across months through generalized additive models. Temporal dependence is captured using a conditional extremes model. By avoiding complex spatial covariance structures, the proposed approach achieves a lightweight yet accurate balance between computational efficiency and statistical fidelity. The method successfully produces regional estimates of exceedance probabilities with associated confidence intervals and effectively quantifies the spatiotemporal patterns of persistent extreme precipitation events.

Technology Category

• Machine Learning: Ensemble Methods
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This article summarises the methods used by the team ``Ca' Foscari" for the EVA 2025 Data Challenge. The questions of the challenge concern the estimation of exceedance probabilities across several locations. Rather than modelling the spatial dependence structure, we reduce the problems to univariate ones by considering relevant spatial order statistics across the sites. Within a Peaks over Threshold framework, we model the marginal distributions of exceedances using generalised Pareto distributions. Generalised additive models are employed to allow the parameters to vary as functions of external predictors, which for all questions are reduced to the month. For questions 1 and 2, the required estimates and confidence intervals are obtained by generating samples from our fitted models. Question 3 involves the dependence between two consecutive observed statistics. To account for this temporal dependence, we fit a conditional extreme value model and derive empirical estimates of persistent extreme events by simulating from this model.

Problem

• extreme precipitation
• exceedance probability
• climate ensembles
• spatial statistics
• temporal dependence

Innovation

• spatial order statistics
• Peaks over Threshold
• generalised Pareto distribution
• generalised additive models
• conditional extreme value model