Predicting hazards of climate extremes: a statistical perspective

TL;DR

This paper develops a statistical framework using Extreme Value Theory to predict and assess the risks of extreme climate-related events like floods and heatwaves in Europe, aiding climate resilience planning.

Contribution

It introduces a novel predictive approach combining EVT with Bayesian methods to evaluate future tail risks and hypothetical worst-case scenarios for climate extremes.

Findings

Predictive models highlight increasing risks under non-stationary conditions.

The approach quantifies uncertainty in extreme event predictions.

Results emphasize the need for proactive risk management strategies.

Abstract

Climate extremes such as floods, storms, and heatwaves have caused severe economic and human losses across Europe in recent decades. To support the European Union's climate resilience efforts, we propose a statistical framework for short-to-medium-term prediction of tail risks related to extreme economic losses and fatalities. Our approach builds on Extreme Value Theory and employs the predictive distribution of future tail events to quantify both estimation and aleatoric uncertainty. Using data on EU-wide losses and fatalities from 1980 to 2023, we model extreme events through Peaks Over Threshold methodology and fit Generalised Pareto (GP) and discrete-GP models using an empirical Bayes procedure. Our predictive approach enables a 'What-if' analysis to evaluate hypothetical scenarios beyond observed levels, including potential worst-case outcomes for a precautionary risk assessment of future extreme episodes. To account for a time-varying behavior of extreme losses and fatalities we extend our predictive method using a proportional tail model that allows to handle heteroscedastic extremes over time. Results of our analysis under stationarity and non-stationary settings raise concerns, reinforcing the urgency of integrating predictive tail risk assessment into EU adaptation strategies.