Climate–Driven Mortality Forecasting Using Deep Learning

Climate extremes have become important drivers of mortality, producing sudden spikes that traditional mortality models fail to predict. To address this gap, we propose a two-step modelling framework that combines a regional weekly Lee–Carter baseline model that captures long-term mortality trends and overall seasonal patterns, with two complementary deep learning architectures designed to model excess mortality driven by environmental conditions and climate shocks. The first, a CNN–LSTM, captures region-specific temporal responses through convolutional filters. The second, a GNN–LSTM, replaces convolutions with graph-based representations to model spatial mortality dependencies and the propagation of climate-related impacts across regions. Both architectures are further extended to a quantile LSTM framework that produces time-varying prediction intervals. We evaluate our models against both the Lee–Carter baseline and MortFCNet (Zheng et al., 2025). Using French regional data over 1990–2019, our models capture delayed and nonlinear associations between environmental extremes and excess mortality. Both proposed architectures outperform the Lee–Carter baseline and MortFCNet across all regions, each reducing test MSE by approximately 24% relative to the MortFCNet, with particularly large gains at the oldest ages where climate-driven mortality spikes are most severe. From a risk management perspective, the proposed framework provides a more realistic characterization of extreme climate-driven mortality risk, with time-varying prediction intervals that offer a more informed basis for the assessment of climate-related longevity exposure by insurers and pension funds.