Predicting hazards of climate extremes: a statistical perspective
提出一个统计框架，基于极值理论预测欧洲极端气候事件（如洪水、热浪）造成的经济损失和死亡人数，支持欧盟的气候适应策略。
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 (EU)’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 losses and fatalities in Europe 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 behaviour 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.