High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.