From Climate Variability to Tourism Comfort: The Role of Seasonal Forecasting

Climate variability is a key factor influencing tourism activity, as weather and climatic conditions largely determine the attractiveness of a destination, the level of comfort experienced by visitors, and the feasibility of outdoor activities. Over recent decades, the relationship between climate and tourism has emerged as a rapidly developing field of research, leading to the development of various approaches and indices aimed at quantifying the impact of climatic conditions on tourism. At the same time, advances in seasonal forecasting have created new opportunities for predicting meteorological and climatic conditions over lead times of several months. Despite this progress, the application of such forecasts in the tourism sector remains relatively limited.

The present study aims to explore the potential of seasonal forecasts for assessing climate suitability for tourism activities, with a particular focus on evaluating the predictive skill of numerical forecasting models in estimating tourism-related climate indices on a seasonal timescale. To this end, hindcast simulations produced by the Weather Research and Forecasting (WRF) model are utilized to derive key climatic variables. Based on these variables, a range of climate-based tourism suitability indices are calculated and analyzed, including widely used metrics such as the Tourism Climate Index (TCI), the Holiday Climate Index (HCI), and the Physiological Equivalent Temperature (PET), as well as indices tailored to specific types of tourism, such as coastal, cultural, and winter tourism.

The analysis focuses on assessing the ability of seasonal forecasts to reproduce the variability of these indices and to provide reliable predictions over lead times of three to six months. By evaluating the performance and limitations of these forecasting tools, the study seeks to determine the extent to which seasonal climate information can support decision-making in the tourism sector. Ultimately, the findings are expected to contribute to a deeper understanding of the relationship between climate variability and tourism comfort, while highlighting the potential of seasonal forecasting as a valuable tool for tourism planning and management.

Acknowledgements: This research was supported by the PREVENT project that has received funding from the EU Horizon Europe framework programme (grant no. 101081276)