  We investigate the changing nature of the frequency, magnitude and spatial
extent of extreme temperatures in Ireland from 1931 to 2022. 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 (not conditioned on observational data) can over-smooth these
events and have trends determined by the specific climate model configuration.
However, climate models do provide valuable information about the detailed
physiography over Ireland and the associated climate response. 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
temporal change in the marginal behaviour of extreme temperature events over
the study domain, which is much larger than the change in mean temperature
levels over this time window. We illustrate how these characteristics result in
increased spatial coverage of the events that exceed critical temperatures.

Methodology