摘要：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 . 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 . Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation . 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 .We present a frst set of comparable spatially explicit global gridded GDP projections with fne 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 fve SSPs, accounting for the two-child policy in China . This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research .
