Spatio-Temporal Modeling for Record-Breaking Temperature Events in Spain
利用西班牙半岛1960-2021年每日最高温度数据，通过分层条件模型分析破纪录温度事件，发现全球变暖趋势使过去十年预期记录数增加近一倍（1.93倍），且变暖速率在空间和季节上高度分化。
Record-breaking temperature events are now very frequently in the news, viewed as evidence of climate change. With this as motivation, we undertake the first substantial spatial modeling investigation of temperature record-breaking across years for any given day within the year. We work with a dataset consisting of over sixty years (1960–2021) of daily maximum temperatures across peninsular Spain. Formal statistical analysis of record-breaking events is an area that has received attention primarily within the probability community, dominated by results for the stationary record-breaking setting with some additional work addressing trends. Such effort is inadequate for analyzing actual record-breaking data. Resulting from novel and detailed exploratory data analysis, we propose rich hierarchical conditional modeling of the indicator events which define record-breaking sequences. After suitable model selection, we discover explicit trend behavior, necessary autoregression, significance of distance to the coast, useful interactions, helpful spatial random effects, and very strong daily random effects. Illustratively, the model estimates that global warming trends have increased the number of records expected in the past decade almost two-fold, 1.93 (1.89,1.98), but also estimates highly differentiated climate warming rates in space and by season.