Tourism climate resources—specialized assets from long-term climatic processes—fall under meteorological resources, with specific value attributes, clear functions and relatively stable states. In modern tourism, travelers usually prioritize checking destination climate when planning itineraries. Climate elements impact travel experiences in four key ways: directly affecting tourism comfort; guiding seasonal travel distribution and destination choice; potentially posing safety risks; and participating in the formation and evolution of tourism landscapes. Thus, systematic research on these resources and their rational development is crucial for sustainable tourism growth.

Mile City’s tourism industry demonstrates rapid development momentum, with its proportion in local GDP steadily increasing year by year (Table 1) ([19]; [10]). Developing tourism has become a key strategic initiative for alleviating fiscal burdens and advancing rural revitalization. With the sequential development of tourism projects like Dongfengyun Characteristic Town and Hushui Water World, coupled with continuous improvements in transportation infrastructure, Mile City’s tourism dominance within Honghe Prefecture is becoming increasingly evident. Against this backdrop, comprehensive exploration of Mile City’s tourism climate resources carries positive practical and theoretical implications for further tapping local tourism potential, optimizing product supply models, enhancing service quality, and ultimately driving high-quality advancement in the city’s tourism sector.

Climate is essentially the most critical natural tourism resource—nearly all world-renowned resorts attract visitors with pleasant climates ([7]). A suitable climate is both a basic requirement for regional tourism development and a primary factor in tourism decision-making ([9]). Research on climate comfort for optimizing climatic tourism resources ([2]) is valuable for scientifically guiding travel decisions ([21]).

Tourism climate comfort evaluation stems from climate comfort assessment, which uses standards or models to comprehensively evaluate meteorological parameters affecting human comfort during tourism ([28]). International scholars began exploring this field in 1920, pioneering work in theories, modeling and applications, and developing empirical models (e.g., effective temperature, wet-bulb blackball index, human comfort meteorological index, temperature-humidity index, wind chill index) for diverse objectives ([24]). Domestic research started in the 1980s: scholars explored the history, core concepts and applicability of models developed over the century ([21]), and conducted extensive studies on model optimization ([18]), improvement ([28]) and practical use. Empirical model research has long dominated China’s tourism climate comfort evaluation. Domestic scholars have carried out such evaluations at different spatial scales [national ([16]; [11]), provincial, city/county, specific attractions ([26]; [1]; [13]; [20]; [3])] and time scales ([23]; [25]), achieving fruitful results.

Table 1.Proportion of tourism revenue in GDP of Mile City in each year (data from the work report of Mile City government).

Paul Peeters et al. used a problem-oriented review to assess climate change mitigation research in sustainable tourism literature. While climate change has become a global existential environmental crisis and tourism emissions keep rising—making it expected to be central to sustainable tourism studies—rigorous system boundaries, lack of shared definitions, and incomplete data in tourism research appear to hinder methods for evaluating tourism’s role in climate mitigation ([14]). Valizadeh Mansoor et al. studied climate change’s impacts on outdoor tourists via the Outdoor Tourism Climate Index (OTCI) survey, using the Change Factor (CF) method to statistically downscale Global Climate Models (GCM) ([17]). Kayal Mohamed argues that international tourism is a major economic sector with steady, substantial growth in developed and developing countries, yet it is unstable due to factors including climate change. He illustrated its environmental and socio-economic impacts using criteria like tourist arrivals, and outlined how climate phenomena interact with international tourism’s commercial aspects. He further explained why destinations must adapt to climate change risks and opportunities, to show tourists the need to prepare for climate change challenges ([6]).

Recent studies have assessed tourism climate resources in various Chinese regions. An analysis of Hanzhong City’s air quality and meteorological data aimed to evaluate its ecotourism climate resources ([27]). Research in Wuwei City utilized the Universal Thermal Climate Index (UTCI) to examine the spatiotemporal distribution of tourism climate comfort ([5]). A comprehensive model integrating multiple indices was developed to assess tourism climate comfort in Guangdong Province ([4]). Similarly, a spatiotemporal analysis of tourism climate comfort was conducted for Luoyang over a 50-year period ([15]).

In research evolution, domestic and international tourism climate studies have built a “theoretical exploration—model construction—regional application” system. However, three gaps remain: Mainstream evaluation models, mostly developed for specific regional climates, lack sufficient cross-regional applicability testing; future research could compare different climate zones to enhance generalizability. Most studies focus on static tourism comfort assessments, with insufficient attention to long-term dynamic changes under climate change; subsequent work should use longer time-series data and scenario simulations to explore trends and drivers. Current research emphasizes natural meteorological factors but inadequately integrates humanistic-social elements (tourist preferences, facility configurations, policy regulation); future studies should add multi-dimensional variables to build a comprehensive evaluation system, providing practical support for precise tourism resource development, spatial optimization, and scientific planning.

The specific technical route of this paper is shown in Figure 1.

Figure 1. Technology roadmap.

1) This study utilizes monthly meteorological data (temperature, relative humidity, wind speed, and solar radiation) for Mile City from 1991 to 2020, sourced from the Yunnan Provincial Meteorological Information Center. Monthly averages were calculated using the following formulas:

Daily Mean Temperature:

Monthly Mean Temperature:

Monthly Mean Relative Humidity:

2) The following method will be used to calculate the actual sunshine hours in Mile City:

① Theoretical sunshine hours: The theoretical sunshine duration (T₀), representing the solar radiation hours receivable by the Earth’s surface in the absence of atmospheric interference, serves as the foundation for calculating actual sunshine duration. This calculation involves geographical latitude and solar declination, utilizing the following classical astronomical formula:

Here, T₀ denotes the theoretical available hours at the horizon (unit: h), ϕ represents the geographical latitude of Mile Ci, and δ is the solar declination angle (unit: ˚), which can be calculated using Spencer’s formula based on the date.

To calculate the monthly theoretical sunshine duration, sum the daily sunshine hours:

Here, n is the number of days in the month.

② Establish a function relationship between adjustment coefficient and humidity. Assuming a simple linear negative correlation exists between the monthly adjustment coefficient km and relative humidity RH_m, which forms the core of the model, expressed as:

Here, a and b are model parameters to be determined.

③ Parameter optimization and constraints

The parameters a and b are optimized through a mathematical iterative algorithm to determine the monthly km values. This process must satisfy the following two constraints:

Trend consistency constraint: The calculated monthly km values should demonstrate a reasonable negative correlation with their corresponding RH_m values.

Annual total constraint: The annual total sunshine duration derived from the optimized km calculation must approach the target value of 2100 hours.

The annual total sunshine duration calculation formula is:

The optimization objective is to minimize ∣S_total − S_target∣.

④ Check whether the seasonal variations of k_m and RH_m align (e.g., low humidity in the dry season corresponds to high k-value, and high humidity in the rainy season corresponds to low k-value).

Figure 2. Calculation process of monthly adjustment coefficient.

Calculate the relative error between the final annual total sunshine hours (S_total) and the target value (S_target) to evaluate the model’s overall accuracy. The formula for relative error calculation is as follows:

The route of the method described in 4.2 (2) is shown in Figure 2.

Temperature and humidity index (THI) quantifies the thermal comfort of environments by integrating temperature and relative humidity through humidity correction. This index evaluates human perception through their interaction with these environmental factors, where higher values indicate more oppressive heat conditions—reflecting actual thermal effects rather than isolated humidity or temperature measurements ([8]). In plain terms, THI simulates human perceived temperature by adjusting the effect of humidity on the body’s heat dissipation capacity, with the core concept being the actual physical sensation of “more stuffy on humid days and more comfortable on dry days at the same temperature”. Widely used in meteorology, agriculture, and indoor environment control, THI follows the calculation formula specified in the “Evaluation Standard for Climate Comfort of Human Settlements” (GB/T 27963-2011):

in Equation (9), THI represents the temperature-humidity index, where T denotes the monthly average temperature (in ˚C) and F indicates relative humidity (in %). Based on research findings regarding tourism climate comfort and human physiological activities, the temperature-humidity index is categorized into nine levels: extremely cold, cold, cool, mild, very comfortable, warm, hot, muggy, and extremely muggy (Table 3).

Table 3. Temperature and humidity index classification and human body comfort and suitability for tourism activities.

The Wind Effect Index (K), a biometeorological composite evaluation metric, is built on three core meteorological elements: wind speed, temperature, and sunshine duration. Its purpose is to scientifically quantify the human body’s physiological comfort under the combined action of these factors, mainly by simulating the heat exchange rate between a unit area of human skin and the external environment within a certain period, thus realizing the quantitative measurement of perceived thermal sensation. In simple terms, K measures actual perceived temperature by combining wind speed’s effect on accelerating heat loss from the human body. It explains the common phenomenon that it is colder on windy days and milder on windless days at the same temperature.

A negative K value means the human body dissipates net heat to the surroundings, leading to a subjective feeling of cold; a positive K value indicates the body absorbs heat from the environment, resulting in a subjective feeling of warmth ([8]). Academically, K systematically integrates meteorological elements, objectively reflects their actual impact on the human thermal balance system, and provides key theoretical basis and data support for constructing scientific indoor climate comfort evaluation systems and promoting accurate public meteorological services.

Table 4. Wind effect index classification and human body comfort and suitability for tourism activities.

In terms of calculation method, K’s specific formula strictly complies with China’s national standard Evaluation of Climate Comfort of Human Settlements (Standard No.GB/T 27963-2011), which is simplified as follows (letters correspond to the subsequent explanation): [K calculation formula: K = f (V, T, S)]

in the calculation model of Equation (10), the parameters are defined as follows: K represents the wind efficiency index, a key indicator for measuring tourism climate comfort; V denotes the average wind speed during the study period, measured in meters per second (m/s); T stands for the average temperature of the corresponding measurement period, measured in degrees Celsius (˚C); S indicates the daily average sunshine duration, measured in hours per day (h/day).

Based on the relevant academic research conclusions on the correlation between tourism climate comfort and human physiological activities, the wind effect index can be divided into 9 different levels. The specific classification criteria and corresponding characteristics of each level are shown in Table 4 ([12]).

In accordance with the “Evaluation of Climate Comfort in Human Settlements” (GB/T 27963-2011) standard, specific classification levels for Wind Effect Index (K) and Thermal Humidity Index (THI) are presented in Table 3 and Table 4 respectively. Within this climate comfort evaluation framework, when discrepancies emerge between Wind Effect Index and THI comfort levels during a given period, the following criteria apply: For seasonal variations, K serves as the primary evaluation metric for winter months, while THI is used for summer months. When monthly average wind speed exceeds 3 m/s, regardless of seasonal variations, K becomes the core evaluation indicator. These standardized procedures are designed to ensure accurate analysis of human settlement climate comfort ([22]).

Based on the meteorological data of Mile City from 1991 to 2020, the monthly average temperature, average relative humidity and average sunshine hours were calculated first, and then the corresponding charts were generated by EXCEL tool. As shown in Figure 3, Mile City’s temperature ranged between 0˚C and 36˚C from 1991 to 2020. The hottest months (with air temperatures exceeding 35˚C) were primarily May, June, and July, while the coldest months occurred in December, January, and February. According to Figure 4, the city’s average temperature during this period (1991-2020) fluctuated between 10.3˚C and 22.9˚C, with minimal annual temperature variations. Combined with Figure 3 and Figure 4, it can be concluded that the months of extreme high temperature and extreme low temperature are basically consistent with the months of average monthly maximum temperature and average monthly minimum temperature, and there is no abnormal temperature phenomenon. According to Figure 5, the monthly average relative humidity in Mile City from 1991 to 2020 ranged between 59% and 81%, with relatively high relative humidity in summer. According to Table 5 and Figure 6, from 1991 to 2020, the strong solar radiation in Mile City is concentrated in March to August, among which April has the strongest solar radiation intensity, exceeding 18,000 kJ/m² day. This is mainly due to the fact that April in Mile City is in spring, when the rainy season has not completely arrived, the precipitation is relatively small, the weather is mostly sunny, and the solar altitude Angle at noon is large.

Figure 3. Monthly statistics of extreme average temperature in Mile City (1991-2020).

Figure 4.Monthly statistics of average temperature in Mile City (1991-2020).

Figure 5. Monthly statistics of average relative humidity in Mile City (1991-2020).

Table 5.Monthly sunshine hours calculated by monthly adjustment coefficient.

Figure 6.Monthly statistics of average solar radiation and sunshine hours in Mile City (1991-2020).

According to the temperature and humidity index formula, the monthly temperature and humidity index results of the same period are shown in Table 6. Figure 7 shows the monthly variation of THI from 1991 to 2020 ([22]).

Table 6. Monthly calculation results of THI in Mile City (1991-2020).

Figure 7. Monthly statistical chart of THI in Mile City (1991-2020).

According to the calculation formula of wind effect index, the monthly wind effect index (K) of Mile City from 1991 to 2020 is calculated as shown in Figure 8.

Figure 8. Monthly statistical chart of wind effect index K in Mile City.

Based on the temperature-humidity index (THI) classification principles outlined in the “Evaluation Standard for Climate Comfort in Human Settlements” (GB/T 27963-2011), tourism-friendly months are categorized into four levels: ① Those with THI between 55 - 70 are designated as suitable for tourism; ② those with THI between 45 - 55 or 70 - 75 are considered moderately suitable; ③ months with THI between 40 - 45 or 75 - 80 require thorough climate preparation; ④ and those with THI below 40 or above 80 are classified as unsuitable for tourism.

Based on the wind effect index classification principles outlined in the “Evaluation Standard for Climate Comfort in Human Settlements” (GB/T 27963-2011) and their summarization, tourism comfort months can be categorized into four levels: ① Months with K values between −600 to −300 and −300 to −200 are designated as ideal tourism conditions, offering optimal comfort and peak travel experiences. ② Months with K values between −800 to −600 and −200 to −50 are considered moderately suitable, featuring mild discomfort (slightly cool or warm) that still allows for basic tourism activities. ③ Months with K values between −1000 to −800 and −50 to 80 require thorough climate preparation, as they present extreme discomfort (either too cold or too hot) necessitating additional cold protection or heatstroke prevention measures. ④ Months with K values below −1200 or above 80 are classified as unsuitable for tourism, delivering both physical discomfort and severely diminished travel enjoyment.

According to the data in Table 7 and Table 8 and the classification principles, Mile City’s most suitable tourism months are April and October. The other suitable months include January, February, March, May, and September. Although March isn’t listed as a highly suitable month, its wind effect index is only 13.3 away from −300. Additionally, with relatively low wind speeds in March, people generally feel comfortable in the temperature, making June, July, and August basically suitable for tourism, without the aforementioned ③ or ④ conditions. Calculation results show that Mile City’s generally suitable tourism months (as indicated above) are mostly due to a warm climate. However, the THI or K values are very close to the standard thresholds for suitable tourism months. The maximum difference is only 1.2 points in June compared to the standard THI value. Therefore, tourists planning to visit Mile City from May to September should make some preparations for summer cooling, while those visiting in January should bring appropriate warm clothing. There’s no need to worry about poor comfort due to severe weather conditions during their trip.

Table 7. Calculation results of temperature and humidity index in summer half year of Mile City (1991-2020).

Table 8. Calculation results of monthly wind effect index in winter half year of Mile City (1991-2020).

Temperature, precipitation, wind speed, sunshine duration, evaporation, and relative humidity are key factors influencing the comfort of a tourist’s climate. Temperature directly affects how hot it feels to humans, while relative humidity and rainfall influence air moisture levels. Wind speed creates wind chill effects, sunshine duration impacts both temperature perception and sunburn risks, and evaporation is closely tied to humidity. These combined elements play a decisive role in determining tourists’ physical experience and comfort level at their destination. The specific impact of the above factors on the climate comfort of Mile City is shown in Table 9.

Through comprehensive analysis, it is evident that Mile City has established a “year-round livable” tourism climate model by leveraging three core climatic advantages: the “health-friendly altitude” (1450 meters, within the human body’s physiological adaptation range), the “natural oxygen bar” (negative oxygen ion concentration of 2500 - 3500 per cubic centimeter), and the “three-dimensional climate” (spanning from South Asian tropical to North Asian subtropical vertical climate zones). From the perspective of tourism planning practices, travelers should integrate seasonal characteristics and spatial variations of these climatic elements when selecting destinations and adjusting travel schedules, thereby maximizing comfort and enjoyment in their travel experiences ([8]).

Table 9. Factors affecting tourism climate comfort in Mile City ([10]).

In order to promote the better development of Maitreya tourism, we can combine the calculation results of suitable/semi-suitable climate in most months of the year, and make breakthroughs from the dimensions of “precise utilization of climate resources + innovative business integration + smart experience upgrading”. The specific methods are as follows (Table 10).

Table 10. Development and utilization measures of tourism climate resources in Mile City.