Malaria remains a leading cause of child morbidity and mortality in sub-Saharan Africa, where the overwhelming majority of global malaria deaths occur and children under five bear the greatest burden. Climatic and environmental conditions strongly shape malaria transmission, but most existing analyses rely on conventional statistical models that may not fully capture the complex, non-linear, and delayed effects of multiple interacting exposures across large spatial scales. This project proposes a continental analysis of malaria risk in children aged 6–59 months across sub-Saharan Africa using Demographic and Health Survey (DHS) data linked to high-resolution environmental exposures, including temperature, precipitation, soil moisture, actual evapotranspiration, and specific humidity. We will analyse approximately 350,000 geo-referenced malaria test records from 26 countries collected between 2006 and 2023. The core methodological innovation is the use of Distributed Lag Neural Additive Models (DLNAM), which combine the interpretability of additive models with the flexibility of neural networks to learn non-linear exposure-response functions and lag structures. This framework will allow us to model how climatic drivers influence malaria risk over time while also assessing modification by socioeconomic and household factors such as wealth, maternal education, sanitation, insecticide-treated net use, and urbanicity. The project is computationally demanding due to the scale of the individual-level dataset, the integration of multiple high-resolution spatiotemporal exposures, and the need for repeated model training, hyperparameter tuning, and validation across countries and subgroups. Access to NAISS Alvis is therefore essential to enable efficient large-scale model development and robust sensitivity analyses. The expected outcome is a more accurate and interpretable characterization of climatic and environmental drivers of childhood malaria across Africa, with direct relevance for early-warning systems, climate-sensitive health planning, and targeted malaria prevention strategies.