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<p>The study of record-breaking values is of significant interest in environmental sciences. Studying records implies analyzing <i>both</i> their occurrence and their magnitude. Further, the study of this phenomenon within a spatio-temporal framework is vital for evaluating seasonal behaviors, identifying spatial patterns, and quantifying the effect of climate change on it. With interest in record-breaking temperatures, we specify models for these observations rather than models for the entire daily temperature stream. Models specifically designed for record-breaking events must consider two random components: the occurrence and the magnitude of each record. With primary interest in the magnitudes, we model the magnitude data <i>given</i> the occurrence data, with the goal of making inference about their evolution within a spatio-temporal framework. We employ a set of 40 geo-referenced time series of daily temperatures across peninsular Spain. From these, we extract the series of occurrences and values of record-breaking events during the summer months, June, July, and August, spanning from 1960 to 2021. The results reveal that the behavior of the increments is neither spatially nor temporally homogeneous, and that there is significant dependence on the previous day: the occurrence of a record increases the posterior mean of the next day’s increment by between 0.3 and 0.6 °C. It is also found that the posterior mean of the average increment on a record-breaking day during the decade 2012–2021 is approximately 1 °C inland, increasing to around 2°C in some coastal areas. After 30 years, mean increments stabilize near 1°C with a mild downward trend.</p>

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Spatio-temporal analysis of record-breaking temperature increments across Spain

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Ana C. Cebrián,
  • Jesús Asín,
  • Jorge Castillo-Mateo,
  • Alan E. Gelfand

摘要

The study of record-breaking values is of significant interest in environmental sciences. Studying records implies analyzing both their occurrence and their magnitude. Further, the study of this phenomenon within a spatio-temporal framework is vital for evaluating seasonal behaviors, identifying spatial patterns, and quantifying the effect of climate change on it. With interest in record-breaking temperatures, we specify models for these observations rather than models for the entire daily temperature stream. Models specifically designed for record-breaking events must consider two random components: the occurrence and the magnitude of each record. With primary interest in the magnitudes, we model the magnitude data given the occurrence data, with the goal of making inference about their evolution within a spatio-temporal framework. We employ a set of 40 geo-referenced time series of daily temperatures across peninsular Spain. From these, we extract the series of occurrences and values of record-breaking events during the summer months, June, July, and August, spanning from 1960 to 2021. The results reveal that the behavior of the increments is neither spatially nor temporally homogeneous, and that there is significant dependence on the previous day: the occurrence of a record increases the posterior mean of the next day’s increment by between 0.3 and 0.6 °C. It is also found that the posterior mean of the average increment on a record-breaking day during the decade 2012–2021 is approximately 1 °C inland, increasing to around 2°C in some coastal areas. After 30 years, mean increments stabilize near 1°C with a mild downward trend.