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.

翻译：我们研究了1931年至2022年间爱尔兰极端温度事件在频率、强度及空间范围上的演变特征。我们构建了一个能够捕捉极端日最高温度数据中时空非平稳性的极值模型。通过广义帕累托分布对边缘变量的尾部进行建模，并采用半参数布朗-雷斯尼克r-广义帕累托过程刻画极端事件的空间依赖性，其中各模型参数允许随时间变化。本研究基于气象站观测数据对极端事件进行建模，这是因为气候模型数据（未经观测数据校正）可能过度平滑极端事件，且其趋势受特定模型构型影响。然而，气候模型确实提供了关于爱尔兰精细地形及其关联气候响应的宝贵信息。我们提出了创新方法，利用气候模型数据克服观测数据稀疏性和采样偏差等问题。分析表明，研究区域内极端温度事件的边缘行为发生了时间变化，其幅度远大于同期平均温度水平的变化。我们通过实例展示了这些特征如何导致超过关键温度阈值的事件空间覆盖范围扩大。