To disentangle the complex nonstationary dependence structure of precipitation extremes
over the entire contiguous United States (US), we propose a flexible local approach based …

Estimating the probability of extreme temperature events is difficult because of limited
records across time and the need to extrapolate the distributions of these events, as …

Abstract Characterization and quantification of climate extremes and their dependencies are
fundamental to the studying of natural hazards. This chapter reviews various parametric and …

Understanding the spatial extent of extreme precipitation is necessary for determining flood
risk and adequately designing infrastructure (eg, stormwater pipes) to withstand such …

Gaussian scale mixtures are constructed as Gaussian processes with a random variance.
They have non-Gaussian marginals and can exhibit asymptotic dependence unlike …

The simultaneous occurrence of extreme events, such as simultaneous storms and floods at
different locations, has a serious impact on risk assessment and mitigation strategies. The …

Simultaneous concurrence of extreme values across multiple climate variables can result in
large societal and environmental impacts. Therefore, there is growing interest in …

Spatial climate data are often presented as summaries of areal regions such as grid cells,
either because they are the output of numerical climate models or to facilitate comparison …

We propose a Bayesian hierarchical model for spatial extremes on a large domain. In the
data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is …

Max-stable processes are increasingly widely used for modelling complex extreme events,
but existing fitting methods are computationally demanding, limiting applications to a few …