Our analysis framework comprised nine main stages, summarized in Extended Data Fig. 1.
Preparation of consistent geotemporal climatologies, 2000–2050
Historical climate data
Climate data were obtained from the Climate Hazards Centre36 and Climate Research Unit gridded Time Series37, downscaled by Worldclim38. Data were gap-filled53, aligned to a standard 5 × 5 km reference grid and aggregated to monthly time-steps. Details of all historical climate data used in this study are provided in Supplementary Information Table 1.
CMIP6 projections
Projections of future climate under SSP 2-4.5 were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)35. These data consist of downscaled and bias-corrected daily CMIP6 multi-model ensemble outputs for historical (2000–2014) and future projection (2015–2050) eras. Data were aggregated to monthly resolution before applying the delta method54 to provide a final calibration to historical climate data described above. To account for between-model uncertainty, 14 models were processed and used for projection of ecologically driven climate impacts (Supplementary Table 2), whereas a thinned subset of three models (ACCESS-CM2, EC-Earth3-Veg-LR and MPI-ESM1-2-LR) was used for the additional analysis of disruptive climate impacts.
Modelling of historical and projected climate suitability indices
The assembled climate data were used in two mathematical models to develop two geotemporal suitability indices: (1) a temperature suitability index (TSI) tracking relative vectorial capacity and (2) a larval habitat suitability index (HSI) measuring relative availability and potential productivity of mosquito larval breeding sites.
Temperature provides both an upper and lower constraint on malaria transmission, reflecting the ectothermic nature of mosquito and parasite lifecycles. Mathematical models linking temperature to relative vectorial capacity are well established and described elsewhere55. Here we updated a degree-day-based framework56,57 to include recently published data on the A. gambiae complex14. The resulting temperature suitability curve is nonlinear, peaking at 26.4 °C.
To describe the relationship between local rainfall, temperature and humidity and the resulting availability of habitat for oviposition and larval development we discretized a Clausius–Clapeyron-based model of habitat availability used in an established mechanistic model of malaria58 as follows: let R t denote rainfall volume at time t, so that transient larval habitat is given by the recursion:
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