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.

翻译：本文总结了“Ca' Foscari”团队为EVA 2025数据挑战赛所采用的方法。该挑战赛的问题涉及多个地点超越概率的估计。我们并未对空间依赖结构进行建模，而是通过考虑站点间相关的空间顺序统计量，将问题简化为单变量问题。在超越阈值峰值框架内，我们使用广义帕累托分布对超越值的边缘分布进行建模。采用广义可加模型允许参数作为外部预测变量的函数而变化，对于所有问题，这些预测变量均简化为月份。对于问题1和2，所需的估计值和置信区间通过从我们拟合的模型中生成样本获得。问题3涉及两个连续观测统计量之间的依赖性。为了考虑这种时间依赖性，我们拟合了一个条件极值模型，并通过从该模型模拟推导出持续性极端事件的经验估计。