Future socioeconomic impacts on climate change are built upon socioeconomic projections and require gridded gross domestic product (GDP) data of a higher resolution for future scenarios 4, 5. The widely used Scenario Model Intercomparison Project (ScenarioMIP) was formed based on different SSPs corresponding to specific representative concentration pathways (RCPs) within Phase Six of the Coupled Model Intercomparison Project (CMIP6) 4. The shared socioeconomic pathways (SSPs), which qualitatively and quantitatively describe possible future patterns of global socioeconomic development under different challenges to climate change mitigation and adaptation 1, are of vital importance in global scientific assessment reports 2, 3 and the current literature 4, 5. This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research. We present a first set of comparable spatially explicit global gridded GDP projections with fine spatial resolutions of 30 arc-seconds and 0.25 arc-degrees for the historical period of 2005 and for 2030–2100 at 10-year intervals under the five SSPs, accounting for the two-child policy in China. Gross regional product of over 800 provinces, which covering over 60% of the global land surface and accounted for more than 80% of GDP in 2005, were used as input. Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation. While global socioeconomic projections are provided mainly at the national level, and downscaling approaches using nighttime light (NTL) images or gridded population data can increase the uncertainty due to limitations. The vulnerability, exposure and resilience of socioeconomic activities to future climate extremes call for high-resolution gridded GDP in climate change adaptation and mitigation research.

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