Economic statistics are frequently produced at an administrative level such as the subnational division. However, these measures may lack sufficient local variation for effective analysis of local economic development patterns and exposure to natural hazards. Agricultural gross domestic product (GDP) is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend for their livelihoods, and it provides a key source of income for the entire household (FAO, 2021). Through a data-fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of agricultural GDP into a global gridded dataset at approximately 10×10km for the year 2010 using satellite-derivedindicatorsofthecomponentsthatmakeupagriculturalGDP,i.e.,crop, livestock, fishery, hunting and forestry production. To illustrate the use of the new dataset, the paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, which amounts to around USD432 billion of agricultural GDP circa 2010, with nearly 1.2 billion people living in those areas. The data are available on the World Bank Development Data Hub (https://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022).