Climate change poses increasing challenges for mortality modeling and underscores the need to integrate climate-related variables into mortality forecasting. This study introduces a two-step approach that incorporates climate information from the Actuaries Climate Index (ACI) into mortality models. In the first step, we model region-specific seasonal mortality dynamics using the Lee-Carter model with SARIMA processes, a cosine-sine decomposition, and a cyclic spline-based function. In the second step, residual deviations from the baseline model are explained by ACI components using Generalized Linear Models, Generalized Additive Models, and Extreme Gradient Boosting. To further capture the dependence between mortality and climate, we develop a SARIMA-Copula forecasting approach linking mortality period effects with temperature extremes. Our results show that incorporating ACI components systematically enhances out-of-sample accuracy, underscoring the value of integrating climate-related variables into stochastic mortality modeling. The proposed framework offers actuaries and policymakers a practical tool for anticipating and managing climate-related mortality risks.

翻译：气候变化对死亡率建模提出了日益严峻的挑战，并突显了将气候相关变量纳入死亡率预测的必要性。本研究提出一种两步法，将精算师气候指数（ACI）中的气候信息整合到死亡率模型中。第一步，我们采用结合SARIMA过程、余弦-正弦分解以及基于循环样条函数的Lee-Carter模型，对特定区域的季节性死亡率动态进行建模。第二步，通过广义线性模型、广义可加模型和极端梯度提升方法，利用ACI各分量解释基线模型的残差偏差。为进一步捕捉死亡率与气候之间的依赖关系，我们开发了一种SARIMA-Copula预测方法，将死亡率周期效应与极端气温相关联。研究结果表明，系统性地纳入ACI分量可显著提升样本外预测精度，这证实了将气候相关变量整合到随机死亡率建模中的价值。所提出的框架为精算师和政策制定者提供了预测和管理气候相关死亡风险的有效工具。