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<p>In the context of climate change, understanding the dependence between meteorological variables is important for adapting actions to reduce the impact of extreme events on populations. This study employs a two-part conditional model to quantify the dependence of precipitation on temperature series based on the data collected in Tulcea (Romania) over 660&#xa0;months. The model separates the process into two parts: (Part 1) occurrence, modeled using Logistic Regression, and (Part 2) intensity, modeled using a Gamma Generalized Linear Model. In Stages 1 and 2, the temperature was standardized. In Stage 3, modeling was performed separately for each month (e.g., January, February, etc.), revealing no significant dependence between the two variables. While in the first two stages, the models do not indicate significant dependences between the variables, in the third stage, the occurrence model shows that the probability of a completely dry month is so rare that the Logistic Regression model is statistically irrelevant. In the intensity model, a strong dependence was found in April, May, July, and September. The consistent negative slopes (− 1.6346, − 1.7745, − 1.8761, and − 2.2719, respectively) in the models for the mentioned months indicate that warmer-than-average months are strongly associated with a decrease in mean precipitation intensity. The validation was done by testing the residuals’ normality and evaluating the models’ adequacy. The model for August, where the temperature effect was only marginally significant, demonstrated excellent structural adequacy (0.976) with residuals passing all three normality tests. The results suggest that data support the hypothesis of decreased precipitation intensity in response to rising temperature.</p>

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Two-part models for the dependence between the monthly precipitation and temperature series: a case study from Romania

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  • 发表日期：

    2026年4月29日

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  • Alina Bărbulescu

摘要

In the context of climate change, understanding the dependence between meteorological variables is important for adapting actions to reduce the impact of extreme events on populations. This study employs a two-part conditional model to quantify the dependence of precipitation on temperature series based on the data collected in Tulcea (Romania) over 660 months. The model separates the process into two parts: (Part 1) occurrence, modeled using Logistic Regression, and (Part 2) intensity, modeled using a Gamma Generalized Linear Model. In Stages 1 and 2, the temperature was standardized. In Stage 3, modeling was performed separately for each month (e.g., January, February, etc.), revealing no significant dependence between the two variables. While in the first two stages, the models do not indicate significant dependences between the variables, in the third stage, the occurrence model shows that the probability of a completely dry month is so rare that the Logistic Regression model is statistically irrelevant. In the intensity model, a strong dependence was found in April, May, July, and September. The consistent negative slopes (− 1.6346, − 1.7745, − 1.8761, and − 2.2719, respectively) in the models for the mentioned months indicate that warmer-than-average months are strongly associated with a decrease in mean precipitation intensity. The validation was done by testing the residuals’ normality and evaluating the models’ adequacy. The model for August, where the temperature effect was only marginally significant, demonstrated excellent structural adequacy (0.976) with residuals passing all three normality tests. The results suggest that data support the hypothesis of decreased precipitation intensity in response to rising temperature.