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<p>We generate datasets quantifying extreme temperature exposure in Europe using a variety of metrics at two sub-national spatial scales (NUTS 2 and NUTS 3) and three temporal scales (daily, extreme temperature wave, and yearly) from 1980-2024. These datasets capture the breadth of temperature metrics used in epidemiology, demography and environmental literature with 67 different metrics: including regionally-unusual temperature events (defined as temperatures above/below the 95<sup><i>t</i><i>h</i></sup>/5<sup><i>t</i><i>h</i></sup> percentile of historical temperatures) and periods of sustained (consecutive day) exposure to extreme temperatures. Although publicly available, climate data format and spatial resolution rarely matches the structure, scale, and extent used to disseminate government statistics on health, economic, and demographic variables, and manipulating raw data is computationally expensive. Here we provide temperature data in a user-friendly format which can easily be linked to EuroStat. Our open-sourced code and reproducible methods can be extended to produce similar datasets at the global scale.</p>

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What’s the TEE: Metrics of Temperature Extremes in Europe NUTS Regions (1980-2024)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Sara R. Ronnkvist,
  • Zoé Haskell-Craig,
  • Abbie Robinson,
  • Risto Conte Keivabu,
  • Mathew E. Hauer,
  • Domenico Bovienzo,
  • Emilio Zagheni

摘要

We generate datasets quantifying extreme temperature exposure in Europe using a variety of metrics at two sub-national spatial scales (NUTS 2 and NUTS 3) and three temporal scales (daily, extreme temperature wave, and yearly) from 1980-2024. These datasets capture the breadth of temperature metrics used in epidemiology, demography and environmental literature with 67 different metrics: including regionally-unusual temperature events (defined as temperatures above/below the 95th/5th percentile of historical temperatures) and periods of sustained (consecutive day) exposure to extreme temperatures. Although publicly available, climate data format and spatial resolution rarely matches the structure, scale, and extent used to disseminate government statistics on health, economic, and demographic variables, and manipulating raw data is computationally expensive. Here we provide temperature data in a user-friendly format which can easily be linked to EuroStat. Our open-sourced code and reproducible methods can be extended to produce similar datasets at the global scale.