Inference for extreme spatial temperature events in a changing climate with application to Ireland

We investigate the changing nature of the frequency, magnitude and spatial extent of extreme temperature in Ireland from 1960 to 2019. We develop an extreme value model that captures spatial and temporal non-stationarity in extreme daily maximum temperature data. We model the tails of the marginal variables using the generalised Pareto distribution and the spatial dependence of extreme events by a semi-parametric Brown-Resnick r-generalised Pareto process, with parameters of each model allowed to change over time. We use weather station observations for modelling extreme events since data from climate models involves abstraction and can over-smooth these events. However, climate models do provide valuable information about the detailed physiography over Ireland. We propose novel methods which exploit the climate model data to overcome issues linked to the sparse and biased sampling of the observations. Our analysis identifies a substantial temporal change in the marginal behaviour, but not the dependence structure, of extreme temperature events over the study domain. We illustrate how these characteristics result in an increased spatial coverage of the events that exceed critical temperatures.