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<p>The Regional Frequency Analysis (RFA) method is widely used to improve probabilistic assessments of extreme hydrometeorological events. However, its classical formulation assumes stationarity - an assumption often violated under climate change. To overcome this limitation, we propose the Non-Stationary Additive Regional Frequency Analysis (NS-Add-RFA), an extension of the additive RFA that integrates nonstationary probabilistic models to represent temporal changes in air temperature frequency distributions at the regional scale. Application of the NS-Add-RFA in controlled simulation experiments and to daily extreme maximum air temperature data from São Paulo State, Brazil (1991–2024), showed that it outperforms nonstationary at-site approaches in capturing the probabilistic structure of extremes under diverse climate and climate change scenarios. The new regionalization method revealed widespread increases in extreme air temperatures across 93% of the state, capturing changes in both the central tendency and dispersion of the series. Furthermore, it provides a more detailed and reliable trend assessment than traditional approaches such as the regional Mann–Kendall trend test. To facilitate broader adoption, we developed the R package NSTempRFA (<a href="https://github.com/gabrielblain/NSTempRFA">https://github.com/gabrielblain/NSTempRFA</a>), which computes all NS-Add-RFA statistics. In conclusion, the NS-Add-RFA offers a scientifically grounded framework for nonstationary regional frequency analysis, enhancing the capacity to assess and interpret extreme air temperature events under changing climate conditions.</p>

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The non-stationary additive regional frequency analysis method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Gabriel Constantino Blain. Blain,
  • Graciela da Rocha Sobierajski,
  • Letícia Lopes Martins

摘要

The Regional Frequency Analysis (RFA) method is widely used to improve probabilistic assessments of extreme hydrometeorological events. However, its classical formulation assumes stationarity - an assumption often violated under climate change. To overcome this limitation, we propose the Non-Stationary Additive Regional Frequency Analysis (NS-Add-RFA), an extension of the additive RFA that integrates nonstationary probabilistic models to represent temporal changes in air temperature frequency distributions at the regional scale. Application of the NS-Add-RFA in controlled simulation experiments and to daily extreme maximum air temperature data from São Paulo State, Brazil (1991–2024), showed that it outperforms nonstationary at-site approaches in capturing the probabilistic structure of extremes under diverse climate and climate change scenarios. The new regionalization method revealed widespread increases in extreme air temperatures across 93% of the state, capturing changes in both the central tendency and dispersion of the series. Furthermore, it provides a more detailed and reliable trend assessment than traditional approaches such as the regional Mann–Kendall trend test. To facilitate broader adoption, we developed the R package NSTempRFA (https://github.com/gabrielblain/NSTempRFA), which computes all NS-Add-RFA statistics. In conclusion, the NS-Add-RFA offers a scientifically grounded framework for nonstationary regional frequency analysis, enhancing the capacity to assess and interpret extreme air temperature events under changing climate conditions.