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Tourism is among the sectors most affected by climate change and natural disasters. Impacts range from direct damage to infrastructure and supply chains to long-term business interruption. Moreover, depending on tourism typology, the consequences of specific hazards can differ substantially: coastal destinations face different challenges than mountain sites. Yet, at the global scale, it remains difficult to identify where climate risks threaten tourism most, and which types of destinations are exposed to which hazards. Here, we present Global Tourism Climate Exposure Layer (G-TCEL), the first disaggregated global tourism impact screening at destination level, designed to reveal climate-risk hotspots for tourism across different tourism landscapes.The assessment builds on three primary components: (1) a global tourism landscape disaggregation to differentiate between modes of tourism, (2) a global tourism density index, and (3) a collection of global climate and disaster risk indicators.The global tourism landscape disaggregation uses topographic, land cover, and demographic data to identify key typologies of tourism activity. We distinguish, for example, between coastal tourism along oceans and lakes, mountain tourism, and urban tourism. These tourism landscapes represent distinct categories of tourism business activity with unique requirements and vulnerabilities. For each square kilometer of the Earth’s surface, weights are assigned to each tourism landscape. To link landscapes with tourism activity, we compiled a tourism density index using global datasets on accommodations, activities, and points of interest. Finally, using e.g. the latest CMIP6 climate model projections, we tailored a suite of global climate risk indicators to the specific vulnerabilities of each tourism landscape.Applying an exposure-at-risk methodology, G-TCEL is a screening in which the tourism landscape at a given location provides weights for the relevance of different climate risks, while tourism density identifies where tourism activity is concentrated. This approach enables us to map and compare destination-level climate exposure for tourism worldwide, highlighting hotspots across coastal, mountain, and urban tourism landscapes. Results are aggregated globally at administrative level 2 and disaggregated by tourism landscape.This work is the companion abstract to Daniell et al. 2026 which provides a subnational global tourism statistics database at multiple levels, part of which is used to estimate tourism density within this screening approach.