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<p>This article investigates the temporal dependence structure and co-occurrence probability of compound climate events, specifically concurrent high temperatures and low precipitation, using a 55-year monthly time series (January 1965–December 2019) from the Tulcea meteorological station in Romania. Traditional univariate analyses often fail to capture the interdependent nature of climate variables, potentially leading to a significant underestimation of climate risk, especially in compound extreme events such as warm-dry periods. To address this limitation, the study employs a rigorous copula-based framework. This methodology, unlike correlation-based or single-variable approaches, rigorously separates the modeling of individual variable distributions (margins) from their joint dependence structure. The analysis is structured around two core directions: (Q1) Investigate if the dependence structure between temperature and precipitation changes over time, and (Q2) determine the exceedance of Maximum Temperature and Non-Exceedance of Minimum Precipitation at various thresholds. To answer (Q1), the study applied four statistical tests—including two-sample tests for Kendall and Spearman, a whole-surface Cramér–von Mises (CvM) copula test, and a direct test on finite-quantile probabilities—to compare "early" (1965–1991) and "late" (1992–2019) sub-periods. These tests were extended through seasonal pooling, rolling Mann–Kendall tests, and a changepoint scan to enhance statistical power and temporal localization. For the first time for the Dobrogea region, the findings show a general stability in the dependence structure across most months, with only mixed, non-coherent changes detected, suggesting that the underlying warm-dry coupling has not undergone a significant, sustained shift that coincides with the early/late split. To answer (Q2), the research quantified the empirical probability of a month being simultaneously unusually warm and unusually dry for various thresholds. This empirical probability was then compared against the independence baseline and against predictions from fitted parametric copulas (Frank and a data-selected family). This comparison revealed the degree to which the observed joint probability exceeds the probability expected under independence. This approach provides a practical, threshold-dependent assessment of compound risk.</p>

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Copula-based assessment of compound warm-dry climate risk: a 55-year analysis of dependence structure stability in Tulcea, Romania

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  • 发表日期：

    2026年3月4日

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  • Youssef Saliba,
  • Alina Bărbulescu

摘要

This article investigates the temporal dependence structure and co-occurrence probability of compound climate events, specifically concurrent high temperatures and low precipitation, using a 55-year monthly time series (January 1965–December 2019) from the Tulcea meteorological station in Romania. Traditional univariate analyses often fail to capture the interdependent nature of climate variables, potentially leading to a significant underestimation of climate risk, especially in compound extreme events such as warm-dry periods. To address this limitation, the study employs a rigorous copula-based framework. This methodology, unlike correlation-based or single-variable approaches, rigorously separates the modeling of individual variable distributions (margins) from their joint dependence structure. The analysis is structured around two core directions: (Q1) Investigate if the dependence structure between temperature and precipitation changes over time, and (Q2) determine the exceedance of Maximum Temperature and Non-Exceedance of Minimum Precipitation at various thresholds. To answer (Q1), the study applied four statistical tests—including two-sample tests for Kendall and Spearman, a whole-surface Cramér–von Mises (CvM) copula test, and a direct test on finite-quantile probabilities—to compare "early" (1965–1991) and "late" (1992–2019) sub-periods. These tests were extended through seasonal pooling, rolling Mann–Kendall tests, and a changepoint scan to enhance statistical power and temporal localization. For the first time for the Dobrogea region, the findings show a general stability in the dependence structure across most months, with only mixed, non-coherent changes detected, suggesting that the underlying warm-dry coupling has not undergone a significant, sustained shift that coincides with the early/late split. To answer (Q2), the research quantified the empirical probability of a month being simultaneously unusually warm and unusually dry for various thresholds. This empirical probability was then compared against the independence baseline and against predictions from fitted parametric copulas (Frank and a data-selected family). This comparison revealed the degree to which the observed joint probability exceeds the probability expected under independence. This approach provides a practical, threshold-dependent assessment of compound risk.