Motivated by the EVA2025 data challenge, where we participated as the team DesiBoys, we propose a regression strategy within the framework of regular variation to estimate the occurrences and intensities of high precipitation extremes derived from different climate runs of the CESM2 Large Ensemble Community Project (LENS2). Our approach first empirically estimates the target quantities at sub-asymptotic (lower threshold) levels and sets them as response variables within a simple regression framework arising from the theoretical expressions of joint regular variation. Although a seasonal pattern is evident in the data, the precipitation intensities do not exhibit any significant long-term trends across years. Besides, we can safely assume the data to be independent across different climate model runs, thereby simplifying the modeling framework. Once the regression parameters are estimated, we employ a standard prediction approach to infer precipitation levels at very high quantiles. We calculate the confidence intervals using a nonparametric block bootstrap procedure. While a likelihood-based inference grounded in multivariate extreme value theory may provide more accurate estimates and confidence intervals, it would involve a significantly higher computational burden. Our proposed simple and computationally straightforward two-stage approach provides reasonable estimates for the desired quantities, securing us a joint second position in the final rankings of the EVA2025 conference data challenge competition.

翻译：受EVA2025数据挑战赛（我们以DesiBoys团队身份参与）的启发，我们提出了一种在正则变化框架内的回归策略，用于估计源自CESM2大型集合社区项目（LENS2）不同气候模拟的高降水极值的发生频率与强度。我们的方法首先在次渐近（较低阈值）水平上经验性地估计目标量，并将其设置为响应变量，该变量源于联合正则变化理论表达式的简单回归框架。尽管数据中存在明显的季节模式，但降水强度在多年间并未表现出任何显著的长期趋势。此外，我们可以合理地假设不同气候模型模拟之间的数据是独立的，从而简化了建模框架。一旦回归参数被估计出来，我们采用标准的预测方法来推断极高分位数处的降水水平。我们使用非参数块自助法程序计算置信区间。虽然基于多元极值理论的似然推断可能提供更精确的估计和置信区间，但会涉及显著更高的计算负担。我们提出的这种简单且计算直接的两阶段方法为目标量提供了合理的估计，使我们在EVA2025会议数据挑战赛的最终排名中获得了并列第二的位置。