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<p>This study presents a novel unified extreme value theory (UEVT) for simultaneous analysis of positive and negative anomalous events derived from the same anomaly series. This framework characterizes the return level–return period relationship, and by defining the critical and tail intensity of <i>N</i>-year events, quantifies the temporal evolution of their intensity and frequency. Based on the UEVT, an interval extreme value distribution (IEVD) is developed to model both the upper and lower tails of anomaly series and for predicting changes of anomalous events with longer return periods. The UEVT and IEVD demonstrate broader applicability and higher accuracy, and&#xa0;improve practical utility compared to the traditional extreme theory and distributions. The results for <i>N</i>-year temperature anomalies suggest that there is a consistent increase in the intensity and frequency of warm events and a decrease in those of cold events under global warming. Regions exhibiting warming holes or cooling blobs, driven by internal climate variability, offer critical areas for future research on climate extremes. Notably, a southward expansion of warm events from the northern high latitudes and the increasing intensity of warm events in tropical regions show new characteristics of climate change. The hindcast intensity of anomalous events under longer return periods agrees well with the observed trend, and is further used to predict 100-year temperature extremes. Additionally, a new prediction method integrating sliding trend with variability provides a new perspective for modeling non-stationary extremes under strong climatic trends. These methods can be extended to the detection and attribution and applied to the future climate projection with climate models.</p>

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The unified extreme value theory for characterizing changes in return periods and levels of N-year temperature anomalies

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Wei Ban,
  • Jianping Li

摘要

This study presents a novel unified extreme value theory (UEVT) for simultaneous analysis of positive and negative anomalous events derived from the same anomaly series. This framework characterizes the return level–return period relationship, and by defining the critical and tail intensity of N-year events, quantifies the temporal evolution of their intensity and frequency. Based on the UEVT, an interval extreme value distribution (IEVD) is developed to model both the upper and lower tails of anomaly series and for predicting changes of anomalous events with longer return periods. The UEVT and IEVD demonstrate broader applicability and higher accuracy, and improve practical utility compared to the traditional extreme theory and distributions. The results for N-year temperature anomalies suggest that there is a consistent increase in the intensity and frequency of warm events and a decrease in those of cold events under global warming. Regions exhibiting warming holes or cooling blobs, driven by internal climate variability, offer critical areas for future research on climate extremes. Notably, a southward expansion of warm events from the northern high latitudes and the increasing intensity of warm events in tropical regions show new characteristics of climate change. The hindcast intensity of anomalous events under longer return periods agrees well with the observed trend, and is further used to predict 100-year temperature extremes. Additionally, a new prediction method integrating sliding trend with variability provides a new perspective for modeling non-stationary extremes under strong climatic trends. These methods can be extended to the detection and attribution and applied to the future climate projection with climate models.