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The increasing demand of ScenarioMIP is calling for GDP projections of high resolution for the future Shared Socioeconomic Pathways (SSPs) in both socioeconomic development and in climate change of adaption and mitigation research. While to date the global GDP projections for five SSPs are mainly provided at national scales, and the gridded data set are very limited. Meanwhile, the historical GDP can be disaggregated using nighttime light (NTL) images but the results are not open accessed, making it cumbersome in climate change impact and socioeconomic risk assessments across research disciplines. To this end, we produce a set of spatially explicit global Gross Domestic Product (GDP) that presents substantial long-term changes of economic activities for both historical period (2005 as representative) and for future projections under all five SSPs with a spatial resolution of 30 arc-seconds. Chinese population in SSP database were first replaced by the projections under the two-children policy implemented since 2016 and then used to spatialize global GDP using NTL images and gridded population together as fixed base map, which outperformed at subnational scales. The GDP data are consistent with projections from the SSPs and are freely available at http://doi.org/10.5281/zenodo.4350027 (Wang and Sun, 2020). We also provide another set of spatially explicit GDP using the global LandScan population as fixed base map, which is recommended at county or even smaller scales where NTL images are limited. Our results highlight the necessity and availability of using gridded GDP projections with high resolution for scenario-based climate change research and socioeconomic development that are consistent with all five SSPs.

A warmer climate is expected to accelerate the global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the socioeconomic benefits of flood defenses (i.e., reduction in population/economic exposure) and the residual risk (i.e., residual population/economic exposure) are poorly understood globally and regionally. To address these knowledge gaps, we use the runoff data from a baseline and 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to drive the Catchment‐based Macro‐scale Floodplain model incorporating the latest satellite river width information. From the simulated annual maxima, we use a Gumbel distribution to estimate the river water depth‐flood return period relationship. We independently evaluate flood impacts on population and economy (i.e., gross domestic product) for a range of flood return periods. We estimate the socioeconomic benefits and the corresponding residual risk for the globe and 26 subcontinental regions. The global population (gross domestic product) exposed to flooding is ∼8% (∼7%) per year lower when implementing existing flood protection infrastructure extracted from the FLOod PROtection Standards database. If the current flood defenses were to be unchanged in the future (Representative Concentration Pathway 4.5 [RCP4.5] and RCP8.5, i.e., ∼2 to ∼4.3°C above the preindustrial levels), the globe and most of the regions (particularly where developing countries are concentrated) would experience an increase in residual risk. This increase is especially obvious when the gap of climate forcing between RCP8.5 and RCP4.5 widens by the end of the 21st century. We finally evaluate the impact of changed flood defense levels on the socioeconomic benefits and the corresponding residual risk.