From One Global Index to Dozens: The Turbulent Evolution of Tourism Climate Scores

Every year, millions of holidaymakers make decisions shaped by something they rarely see: a number. Behind glossy destination brochures and travel advisories sit tourism climate indices, mathematical tools that convert raw meteorological data into scores of how suitable a place is for tourism, and when the best conditions arrive. A new open-access review in Theoretical and Applied Climatology, authored by Ariel S. Prinsloo and Jennifer M. Fitchett of the University of the Witwatersrand, has for the first time mapped the entire landscape of these indices, analysing 187 studies published between 1985 and the end of 2025. The verdict is a field in vigorous but uneven evolution, where the original index still dominates, new tools multiply, and the assumptions baked into the maths are increasingly under scrutiny.

The story begins in 1985, when Zbigniew Mieczkowski introduced the Tourism Climate Index, or TCI, computed at a coarse global scale from monthly climate normals. The index combined thermal comfort, sunshine, precipitation and wind into a single rating of climatic favourability for general tourism. For sixteen years after its debut, the review found, no other indices were used at all. Then, from the 2000s, interest surged: researchers began applying the TCI to individual countries, and a wave of adaptations followed. The TCI remains the most widely used index to date, largely because of its broad applicability across what the authors describe as world tourism attractions, but its dominance has not gone unchallenged.

The critiques are technical and pointed. Researchers have attacked the TCI’s subjective, expert-derived rating and weighting systems, its limited integration of how tourists actually experience weather and behave in response to it, and the dominance of certain variables such as precipitation and wind, whose thresholds may not transfer across climatic contexts and can disproportionately distort overall scores. Its low temporal resolution, typically monthly, fails to capture the daily and intra-monthly variability that governs real tourism decisions. And its generic structure, critics argue, inadequately reflects the very different climatic requirements of, say, a skier and a sunbather. These objections have driven the development of second-generation and sector-specific indices designed to fix each weakness in turn.

The first major rethinking came in 2000, when Morgan and colleagues built the Beach Climate Index, discarding mean 24-hour temperatures because beach activity is essentially a daytime pursuit, and replacing general thermal comfort with the specific thermal sensation beach tourists seek. In 2008, de Freitas and colleagues proposed the Climate Index for Tourism, the CIT:3S, built around what they called the essential features of a second-generation index: theoretical soundness, comprehensiveness, simplicity, sensitivity to overriding weather conditions, and empirical testing, verified through 331 surveys of university students. That validation sample itself has since drawn criticism, because students are not representative of all tourists, and because people’s perception of thermal conditions can be strikingly inaccurate. Meanwhile, Yu and colleagues developed a modified index using hourly data on lightning, snow and hail, and Perch-Nielsen and colleagues recalculated the TCI at a daily resolution, allowing the frequency of favourable days to be tracked through seasons and years. Kovács and Unger later produced the mTCI for central Europe, substituting Physiologically Equivalent Temperature for effective temperature and shifting to ten-day averages matched to typical vacation lengths.

The most consequential recent family of tools is the Holiday Climate Index, developed by Scott and colleagues in 2016 as a purported replacement for both the TCI and the CIT:3S. Based on tourists’ stated weather preferences gathered from questionnaires, the HCI: Urban made a bold assumption: night-time thermal comfort could be dropped, because in the three decades since the TCI was created, air conditioning had become almost universal in tourist accommodation in developed countries and major destinations in developing ones. That assumption, the review shows, is problematic. In the Global South, air conditioning is far from ubiquitous and electricity grids are constrained. More strikingly, when Mnguni and Fitchett interrogated the six European cities for which the index was originally developed, they found that even within the Global North the assumption of universal air conditioning did not hold true, a finding that concurs with the Tourism Panel on Climate Change. Given growing concern about the health impacts of hot nights and heat-disrupted sleep, excluding night-time conditions may systematically flatter destinations that are, in practice, uncomfortably hot after dark.

The HCI was soon adapted for beaches, validated against monthly arrivals data from Canadian tourists in Antigua and Barbuda, Barbados and Saint Lucia, and has since been applied from China and Japan to Sri Lanka, Greece and Réunion Island. A global assessment of the HCI: Beach from 1950 to 2019 found favourable beach conditions shifting poleward under climate change, favouring previously marginal destinations over established ones. In parallel, the Camping Climate Index emerged from US camping occupancy data, and unlike the HCI variants it deliberately retains night-time temperature, because campers sleep outdoors. It also includes exception statements that automatically classify conditions as unsuitable when thresholds for temperature, precipitation or wind are exceeded. The Ski Climate Index followed, integrating fuzzy logic, literature analysis and co-creation with the ski industry to combine snow reliability, snowmaking capacity and peak operational periods. Yet the review raises a pointed question: as indices become ever more specialised, such as the Ice and Snow Sport Climate Index for China, do they still serve tourism climatology, or are they diverging into sport-specific assessments?

Geographically, the field is strikingly lopsided. The Mediterranean leads with 35 studies, followed by Europe and Asia with 23 each and the Middle East with 21, while Australia has just three, Africa outside its south has three, and Latin America has none at all. Only twelve global studies have been conducted, ten of them using the TCI. The imbalance reflects disparities in meteorological data infrastructure, institutional resources and established tourism markets, and it matters because global gridded assessments can obscure critical local nuance. In South Africa, global Camping Climate Index outputs classify the country as acceptable to good, whereas local calculations using ground-based data yield substantially lower ratings, from unfavourable to acceptable. Similarly, global TCI calculations for South Africa in January achieved excellent ratings, but local analyses reveal substantial spatial variability within the country. Local-scale applications, the authors argue, are not mere tick-box exercises but essential contributions to understanding tourism-climate interactions.

Methodologically, the field is also shifting from rivalry to ensemble thinking. Tourism climatology is unusual, the review notes, in that new indices are often pitched as replacements for their predecessors, and early multi-index studies were designed to prove one index’s dominance, most visibly in work pairing the TCI with the Holiday Climate Index to argue the latter should take over. But the HCI variants are sub-sector specific, and the authors contend that value comes from interpreting both sets of results for the tourism types they represent, not from crowning a winner. Recent studies increasingly apply several indices side by side, mirroring the model-intercomparison approach of the IPCC: Ma and colleagues applied five indices across eleven US national parks, finding the Camping Climate Index most useful for predicting visitation, while work on Réunion Island applied four indices to a small but geographically diverse destination. Crucially, the indices are not mathematically merged; each is computed independently with a shared thermal comfort component and compared across annual scores, seasonal distributions and rates of change, because different forms of tourism carry different climatic sensitivities.

The review closes with a roadmap. Before any index is applied, the authors argue, researchers should conduct a pre-assessment of its applicability, suitability and validity for the destination in question, rather than relying on retrospective validation through questionnaires and online reviews after the fact. Automated scores now available from the Copernicus database for the CIT:3S and HCI: Urban offer quick overviews but should be interrogated against ground-based data. Underrepresented sectors, from hiking and desert tourism to yachting and fishing, need new indices built on expert thresholds and empirically triangulated against tourist preferences and observed visitation data. And perhaps most urgently, the insights locked in peer-reviewed papers need translating into policy briefings, operator engagement and accessible materials, so that four decades of quantified climatic suitability can actually inform climate-resilient tourism planning in an era of accelerating environmental change.

Subject of Research: The development, application and critical evaluation of tourism climate indices for assessing destination climatic suitability

Article Title: A critical examination of the tourism climate index landscape

Article References: Prinsloo, A. S., & Fitchett, J. M. (2026). A critical examination of the tourism climate index landscape. Theoretical and Applied Climatology, 157(10), Article 652. https://doi.org/10.1007/s00704-026-06574-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06574-4

Keywords: tourism climate index, climatology, Holiday Climate Index, thermal comfort, climate change, tourism planning, biometeorology, Mieczkowski, multi-index approach, Global South, beach tourism, validation