We present the winning strategy for the EVA2025 Data Challenge, which aimed to estimate the probability of extreme precipitation events. These events occurred at most once in the dataset making the challenge fundamentally one of extrapolating extreme values. Given the scarcity of extreme events, we argue that a simple, robust modeling approach is essential. We adopt univariate models instead of multivariate ones and model Peaks Over Thresholds using Extreme Value Theory. Specifically, we fit an exponential distribution to model exceedances of the target variable above a high quantile (after seasonal adjustment). The novelty of our approach lies in using martingale testing to evaluate the extrapolation power of the procedure and to agnostically select the level of the high quantile. While this method has several limitations, we believe that framing extrapolation as a game opens the door to other agnostic approaches in Extreme Value Analysis.

翻译：本文介绍了EVA2025数据挑战赛的获胜策略，该挑战旨在估计极端降水事件的概率。由于数据集中极端事件最多仅出现一次，该挑战本质上属于极值外推问题。鉴于极端事件的稀缺性，我们认为采用简单且稳健的建模方法至关重要。我们采用单变量模型而非多变量模型，并基于极值理论对阈值超越峰值进行建模。具体而言，我们通过拟合指数分布来模拟目标变量在高于高分位数（经季节调整后）的超越量。本方法的新颖之处在于利用鞅检验评估流程的外推能力，并以不可知论方式选择高分位数的水平。尽管该方法存在若干局限性，但我们认为将外推问题构建为博弈框架，为极值分析中其他不可知论方法开辟了新途径。