This dataset provides ensemble simulations of the Aflatoxin Index (AFI) across the Euro–Mediterranean domain under historical and future climate change scenarios. AFI is a dimensionless indicator derived from the validated AFLA-maize mechanistic model (https://doi.org/10.1016/j.compag.2013.03.005), which integrates maize phenology with Aspergillus flavus infection dynamics. The dataset supports assessments of climate-driven food safety risks in one of the world’s most important maize-producing regions. Climate data were obtained from the ISIMIP3b (https://www.isimip.org/) bias-adjusted and downscaled outputs of three CMIP6 global circulation models (GFDL-ESM4, IPSL-CM6A-LR, MRI-ESM2-0). Scenarios include: · Historical (1981–2014) · SSP1-RCP1.9, ‘SSP119’ (2015–2050) · SSP1-RCP2.6, ‘SSP126’ (2015–2050) · SSP3-RCP7.0, 'SSP370' (2015–2050) For each scenario, AFI simulations were run at ~50 km grid resolution, covering Europe, North Africa, the Middle East, and western Russia (25°–75°N, –23.75°–44.75°E). Ensemble means were generated to reduce uncertainty across climate models. Data are provided in NetCDF format, including the following dimensions and variables: · latitude, longitude: grid coordinates (decimal degrees, WGS84) · year: 34 years for historical, 36 years for each future scenario · AFI: ensemble average Aflatoxin Index (dimensionless). Ocean grid points are set to NaN. The dataset underpins analysis presented in “Missing the 1.5°C Target: What’s Next for Aflatoxin Contamination?” (P.Toscano, M.Pasqui, M. Camardo Leggieri, D. Balková, P. Battilani, under review), in which further methodological details and results are described. All simulations and processing were performed in MATLAB (MathWorks, 2024). Seamask processing used the MATLAB function landmask by Chad Greene, (landmask, https://www.mathworks.com/matlabcentral/fileexchange/48661-landmask), MATLAB Central File Exchange. Accessed December 05, 2024. The dataset is intended for researchers, policymakers, and stakeholders interested in climate change impacts on food safety, predictive mycotoxin modelling, and Euro–Mediterranean agricultural risk assessment. It can be used to evaluate regional vulnerabilities, support trade and food security studies, and inform adaptation and mitigation strategies.