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In this study, we begin a comprehensive characterisation of temperatureextremes in Ireland for the period 1981-2010. We produce return levels ofanomalies of daily maximum temperature extremes for an area over Ireland, forthe 30-year period 1981-2010. We employ extreme value theory (EVT) to model thedata using the generalised Pareto distribution (GPD) as part of a three-levelBayesian hierarchical model. We use predictive processes in order to solve thecomputationally difficult problem of modelling data over a very dense spatialfield. To our knowledge, this is the first study to combine predictiveprocesses and EVT in this manner. The model is fit using Markov chain MonteCarlo (MCMC) algorithms. Posterior parameter estimates and return levelsurfaces are produced, in addition to specific site analysis at synopticstations, including Casement Aerodrome and Dublin Airport. Observational datafrom the period 2011-2018 is included in this site analysis to determine ifthere is evidence of a change in the observed extremes. An increase in thefrequency of extreme anomalies, but not the severity, is observed for thisperiod. We found that the frequency of observed extreme anomalies from2011-2018 at the Casement Aerodrome and Phoenix Park synoptic stations exceedthe upper bounds of the credible intervals from the model by 20% and 7%respectively.

Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland