||One of the challenges in globally consistent assessments of physical climate risks is the fact thatasset exposure data are either unavailable or restricted to single countries or regions. We introduce a globalhigh-resolution asset exposure dataset responding to this challenge. The data are produced using “lit population”(LitPop), a globally consistent methodology to disaggregate asset value data proportional to a combination ofnightlight intensity and geographical population data. By combining nightlight and population data, unwantedartefacts such as blooming, saturation, and lack of detail are mitigated. Thus, the combination of both data typesimproves the spatial distribution of macroeconomic indicators. Due to the lack of reported subnational assetdata, the disaggregation methodology cannot be validated for asset values. Therefore, we compare disaggregatedgross domestic product (GDP) per subnational administrative region to reported gross regional product (GRP)values for evaluation. The comparison for 14 industrialized and newly industrialized countries shows that thedisaggregation skill for GDP using nightlights or population data alone is not as high as using a combinationof both data types. The advantages of LitPop are global consistency, scalability, openness, replicability, and lowentry threshold. The open-source LitPop methodology and the publicly available asset exposure data offer valuefor manifold use cases, including globally consistent economic disaster risk assessments and climate changeadaptation studies, especially for larger regions, yet at considerably high resolution. The code is published onGitHub as part of the open-source software CLIMADA (CLIMate ADAptation) and archived in the ETH DataArchive with the link https://doi.org/10.5905/ethz-1007-226 (Bresch et al., 2019b). The resulting asset exposuredataset for 224 countries is archived in the ETH Research Repository with the link https://doi.org/10.3929/ethz-b-000331316 (Eberenz et al., 2019).