  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.
