Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. In many applications, not only the frequency but also the magnitude of concurrent extremes are of interest. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.

多个气候变量同时出现极端值可能会导致巨大的社会和环境影响。因此，人们越来越有兴趣了解这些并发的极端情况。在许多应用中，不仅频率而且并发极值的大小都很重要。解决这个问题的一种方法是研究一个气候变量的分布，因为另一个是极端的。在这项工作中，我们开发了一个统计框架，用于通过条件方法估计双变量并发极端值，其中使用分位数回归和极值分析技术将单变量极值建模与条件尾部分布的依赖建模相结合，以量化并发极端值。我们关注以日降水量取季节性最大值为条件的日风速分布。加拿大区域气候模型大型集合用于通过具有指定依赖结构的模拟研究和通过对气候模型模拟的依赖结构的分析来评估拟议框架的性能。