The evaluation of climate comfort for tourism can provide information for tourists selecting destinations and tourism operators. Understanding how climate conditions for tourism evolve is increasingly important for strategic tourism planning, particularly in rapidly developing tourism markets like China in a changing climate. Multidimensional climate indices are needed to evaluate climate for tourism, and previous studies in China have used the much criticized “climate index” with low resolution climate data. This study uses the Holiday Climate Index (HCI) and daily data from 775 weather stations to examine interregional differences in the tourist climate comfortable period (TCCP) across China and summarizes the spatiotemporal evolution of TCCP from 1981 to 2010 in a changing climate. Overall, most areas in China have an “excellent” climate for tourism, such that tourists may visit anytime with many choices available. The TCCP in most regions shows an increasing trend, and China benefits more from positive effects of climate change in climatic conditions for tourism, especially in spring and autumn. These results can provide some scientific evidence for understanding human settlement environmental constructions and further contribute in improving local or regional resilience responding to global climate change.

Climate change and its fluctuations exert significant impacts on the tourism industry, particularly through the influence of high temperatures as typical meteorological and climatic factors on tourists’ travel intentions, spatial behavior preferences, and destination choices. This study employs China as a case study to investigate the effects of high-temperature weather on tourism and tourist travel. By analyzing news reports, conducting observations, and examining statistics, an exploratory analysis of tourism in China under high-temperature scenarios reveals several noteworthy findings. Firstly, tourists seeking relief from the summer heat exhibit a preference for short-distance trips and destinations rich in natural resources. Secondly, heat-escape tourism products have gradually transformed over time, evolving from mountain heat escapes in the 1980s to waterfront vacations in the 1990s, artificial water leisure in the 2000s, and ultimately culminating in the development of heat-escape cities in the 2010s. Additionally, this study examines interregional disparities in summer tourism climate amenity across China using the Holiday Climate Index (HCI), the Tourism Climate Index (TCI), and daily data from 775 weather stations. It also provides a summary of the spatiotemporal evolution from 1961 to 2020 within the context of climate change, revealing intriguing findings. Moreover, a case study of Shanghai Disneyland demonstrates the greater significance of the holiday system compared to temperature constraints. This study aims to examine the interaction between high temperatures and China’s tourism in the context of climate change, providing a scientific foundation for government agencies and tourism enterprises to develop effective policies and plans.

In an open economic system, tourism spillover occurs widely between cities, making it a useful index for regional tourism cooperation or competition. This article improves the gap model on tourism spillover between two cities, suggested by S. Li and Wang(2009a Li, S. and Wang, Z. 2009a. Computable models on regional spillovers of tourism industry: A case study of the Yangtze River Delta. Tourism Tribune, 24(7): 18–26. [Google Scholar] ), in three aspects: (a) an impact factor is added to the model; (b) more objective indicators are applied to the definitions of variables; and (c) more rational methods are used to estimate parameters. The case of the Yangtze River Delta is reexamined based on the improved model. The results show that the new gap model explains tourism spillover more effectively than the original one; it could also provide more help and advisement in developing regional tourism cooperation.

Seasonal prediction uses ensemble forecasting to sample the distribution of possible climate outcomes in the upcoming term given the slowly-varying constraints on the atmosphere. However, translating the members’ distribution of a seasonal forecast into meaningful information is a challenge climate services are often faced with. When a large ensemble spread makes the forecast difficult to interpret, highlighting the competing signals from which the uncertainty arises may bear added value to end users. In order to do so, we present an approach to extract alternative seasonal forecast scenarios over Europe (in temperature, precipitation and atmospheric circulation) from ensemble seasonal forecasts. The aim of the scenarios is to refine the ensemble analysis beyond the usual forecast products (e.g ensemble mean, tercile probabilities), and to provide additional guidance for preparation of the seasonal forecast bulletins routinely issued at Météo-France. The seasonal forecast scenarios are determined with a hierarchical clustering of the ensemble members, based on their forecast temperature at 2-m (T2m). The dissimilarity between two members is defined from the spatial correlation between their respective maps of T2m anomalies – relative to model climatology – over a European domain (29.5°W-40.5°E; 30.5°N-70.5°N, land grid points only). The subsequent dissimilarity matrix across the ensemble feeds the clustering algorithm that groups members into clusters eventually defining the scenarios. The seasonal outcomes corresponding to these scenarios are then described through several diagnostics, e.g composites on sensible climate variables (T2m, precipitation), composites on atmospheric circulation variables (Z500, V200), and analysis through modes of variability and weather regimes. In addition, we provide a description of how scenarios diverge in the course of forecast integration and identify teleconnections related to each scenario. Finally, we also assess the skill of the seasonal forecasts assuming that only the subset of members representing the most likely scenario is retained. This methodology has been implemented to the Copernicus Climate Change Services (C3S) real-time seasonal forecasts across the past year for experimental purposes, and it is shown to be a relevant complement for the preparation of the Météo-France operational seasonal bulletins.