A High-Resolution, Long-Term, Comprehensive Climate Comfort Index Dataset for China (1961–2022)

Abstract

Climate comfort constitutes a pivotal metric for assessing the influence of climate change on human leisure and tourism pursuits, while existing studies on climate comfort face notable limitations. First, the predominant approach relies on indices that incorporate only single meteorological variables, thereby restricting their capacity to encapsulate the holistic human perception of the climatic milieu. Second, there is a dearth of long-term (exceeding 30 years) and high-spatial-resolution comprehensive climate comfort indices, impeding thorough examinations of long-term climatic variability. Consequently, this study endeavors to develop a long-term, high-spatial-resolution comprehensive climate comfort index tailored to China. Utilizing monthly gridded meteorological data at a resolution of 0.25° × 0.25° spanning from 1961 to 2022, the temperature-humidity index, wind effect index, and clothing index were computed. Three weighting methodologies—namely, the expert scoring approach, the entropy weight method, and the Bayesian three-cornered hat method—were employed to generate three distinct sets of comprehensive climate comfort indices. Following this, uncertainties were quantified via the Bayesian three-cornered hat method, and the three indices were synthesized into a definitive composite climate comfort index. The ensuing dataset provides the high-resolution, long-term comprehensive climate comfort index for China, encompassing over six decades. It delineates spatial heterogeneity and seasonal dynamics, offering data for evaluating the ramifications of climate change on human leisure and tourism activities. The dataset is available at: https://doi.org/10.11888/Atmos.tpdc.303390.

Background & Summary

Over the past two decades, the global mean surface temperature has risen by approximately 1.0°C above the 1850–1900 baseline¹. This warming trend has coincided with a marked increase in the frequency and intensity of extreme climate events^2–4. These climatic shifts have exerted profound effects on human tourism activities and outdoor recreational behaviors^5–7. Such changes have significantly reshaped regional climate comfort patterns, directly influencing leisure and tourism practices⁸. Variations in key meteorological parameters—such as temperature and humidity—directly impact human thermal balance and adaptive capacity⁹, thereby influencing the quality of leisure destination experiences and travel intentions¹⁰. Consequently, precisely assessing and elucidating the spatiotemporal dynamics of climate comfort under climate change is essential for understanding its implications for leisure and tourism. Establishing a scientifically rigorous climate comfort index forms the cornerstone of such research.

Climate comfort denotes the human body’s thermal perception and overall environmental suitability in response to multiple meteorological factors, including temperature, humidity, wind speed, and solar radiation^11,12. A range of single-factor climate comfort indices have been developed, such as the temperature-humidity index (THI¹³), wind effect index (WEI¹⁴), physiological equivalent temperature (PET⁹), and clothing index (ICL¹⁵). These indices have been extensively applied in regional climate comfort assessments^11,16,17. However, these indices predominantly focus on individual meteorological variables—for instance, THI centers on temperature and humidity, while the WEI primarily addresses wind speed—rendering them inadequate for capturing the holistic human perception of the climatic environment and the complexities of global warming.

To overcome the constraints of relying on single indices in climate comfort evaluation, researchers commonly synthesize multiple individual indices (e.g., THI, WEI, ICL) to construct composite indices that capture the multidimensional nature of climate comfort. For example, Ma et al. (2009) applied an expert scoring technique to allocate weights of 0.6, 0.3, and 0.1 to the THI, WEI, and ICL, respectively, thereby formulating a comprehensive climate comfort index to appraise the climate comfort of 41 tourist cities in China¹⁸. Sun and Yu (2014) employed the same approach to devise a composite climate index for assessing 19 tourist cities throughout China¹⁹. Likewise, Cao et al. (2015) adopted this method to establish a holistic climate comfort index for the Altay Mountains region in China²⁰. Although the expert scoring method utilized by these scholars ensures ease of implementation, the resulting weight assignments are inherently subjective. Yu et al. (2020) formulated the Holiday Climate Index (HCI) by integrating thermal comfort, aesthetic, and physical dimensions via expert-determined weights, and subsequently evaluated tourist climate comfort across mainland China from 1981 to 2010²¹. These instances of expert-based weights underscore that the assigned weights remain static across space, neglecting the pronounced spatial heterogeneity of meteorological variables and their effects, which necessitate dynamic adaptation. This shortcoming undermines the objectivity and spatial adaptability of evaluation outcomes generated by composite indices.

Objective weighting approaches effectively mitigate the subjectivity associated with indicator weight determination. Among these, the entropy weight method is a widely adopted objective technique. For example, Xiao et al. (2025) utilized the entropy weight method to weight two indicators—the tourism climate index (TCI) and holiday climate index (HCI)—to formulate a composite tourism climate index for evaluating climate comfort in Yunnan Province²². Concurrently, the Bayesian three-cornered hat (BTCH) method, as an objective technique for data fusion and uncertainty assessment, is extensively employed in data integration within geographical research. For instance, He et al. (2020) applied the BTCH method to fuse evapotranspiration data²³. These studies underscore the efficacy of BTCH in refining weight allocation and quantifying uncertainties, suggesting that applying the BTCH method to determine weights for climate comfort indices holds substantial promise for developing composite climate indices.

Consequently, leveraging monthly high-resolution (0.25° × 0.25°) gridded meteorological data, this study computes three individual climate comfort indices: the temperature humidity index (THI), the wind effect index (WEI), and the clothing index (ICL). It systematically contrasts two categories of weighting methods—objective methods (the entropy weight method and the BTCH method) and the subjective expert scoring method—to construct three corresponding composite climate comfort indices. Building on this, the spatial uncertainties of these three index sets are assessed, and their weights are assigned based on uncertainty levels, ultimately integrating them into a cohesive composite climate comfort index dataset. This research seeks to deliver an objective and spatially adaptable composite climate comfort index for China, to support a better understanding of how climate change influences climate comfort and human activities.

Methods

Meteorological data

Daily observational data—encompassing temperature (°C), wind speed (m/s), relative humidity (%), and sunshine duration (h)—are sourced from the CN05.1 gridded dataset²⁴ and employed to compute climate comfort indices. This dataset is underpinned by observations from over 2,400 stations administered by the China Meteorological Administration (https://data.cma.cn/). CN05.1 represents an enhancement of CN05²⁵, based on interpolation from a larger number of station observations (760 for CN05 vs. 2,416 for CN05.1) and is characterized by a higher spatial resolution of 0.25° × 0.25° (latitude–longitude). Daily gridded data were produced using the anomaly method: first, a gridded mean climatology (1971–2000) was calculated via thin-plate smoothing splines; second, a gridded daily anomaly derived through an angular weighting method was added; and finally, the two components were integrated^24,26. The temporal scope covers the period from 1 January 1961 to 31 December 2022, while the spatial extent ranges from 14.75°N to 55.25°N in latitude and 69.75°E to 140.25°E in longitude. Owing to the absence of observational data, Taiwan Province is omitted, and thus the derived indices cover only mainland China. It should be noted that the daily observational data were first aggregated to a monthly scale before the climate comfort indices were calculated accordingly.

Climate comfort indices

Temperature-humidity index (THI)

The Temperature-Humidity index (THI) synthesizes air temperature and relative humidity to characterize the thermal exchange between the human body and its ambient environment¹⁹. As a fundamental metric for assessing perceived climate comfort, the THI is defined by the following equation:

$$\mathit{THI}=\left(1.8\times T_a+32\right)-0.55\times \left(1-\mathit{rhu}\right)\times \left(1.8\times T_a-26\right)$$ 1

where T_a denotes air temperature (°C), and rhu denotes relative humidity (%).

Wind Effect Index (WEI)

The Wind Effect Index (WEI) integrates the influences of wind speed, air temperature, and sunshine duration on thermal comfort¹⁴. The index is computed using the equation:

$$\mathit{WEI}=-\left(,(,10\times \sqrt{V}+10.45-V\right)\times \left(33-T_a\right)+8.55\times \mathit{SSD}$$ 2

where V represents the wind speed (m/s), and SSD denotes sunshine duration (h/day).

Clothing Index (ICL)

The Clothing Index (ICL) is grounded in the principle of thermal equilibrium between the human body and the ambient environment^11,16. The index leverages common meteorological parameters—namely, air temperature, wind speed, and radiation—utilized in empirical models, thereby balancing physical interpretability with computational efficiency. The calculation is expressed as:

$$\mathit{ICL}=\frac{T_s-T_a}{0.155\times H}-\frac{I_a\left(H+R\right)}{H}$$ 3

where T_s is the skin comfort temperature (typically 33 °C¹⁶); T_a is air temperature (°C) and I_a is the thermal resistance of the clothing surface layer, calculated as $I_a=\frac{1}{0.62+0.19\times \sqrt{V}}$, where V is the wind speed (cm/s). Since meteorological stations measure wind speed in m/s, it is necessary to convert the wind speed to cm/s. R is the net solar radiation heat load on the human body surface (W/m²), and H is the rate of dry heat transfer to the surrounding environment, representing 75% of the metabolic rate. For light activity, the metabolic rate is generally set as 116 W/m^2 15, resulting in H = 116 × 0.75 = 87 W/m².

Consequently, Eq. (3) can be reformulated as:

$$\mathit{ICL}=\frac{33-T_a}{13.485}-\frac{87+R}{87\times \left(0.62+0.19\times \sqrt{V}\right)}$$ 4

where T_a and V are obtained directly from meteorological station records. The critical step in calculating the ICL is determining R, the net solar radiation heat load, which follows the methodology detailed in Liu et al.¹¹.

Composite climate comfort index (CCI)

This study utilizes three weighting methods—the expert scoring method (ESM), the entropy weight method (EWM), and the Bayesian three-cornered hat (BTCH) method—to assign weights to the temperature-humidity index (THI), wind effect index (WEI), and clothing index (ICL), respectively, thereby constructing corresponding composite climate comfort indices.

Among these, the ESM is a subjective weighting approach that has been employed in prior research to develop composite comfort indices for China^19,20. Consistent with these studies, this research assigns weights of 0.6, 0.3, and 0.1 to THI, WEI, and ICL, respectively, to establish the ESM-based composite climate comfort index (CCI_ESM). The weighting principles and procedures of the EWM and the BTCH method are detailed in the subsequent sections.

Entropy weights method (EWM)

The EWM is an objective weighting technique that determines indicator weights based on information entropy²⁷. A higher entropy value signifies greater uncertainty in an indicator, implying a more substantial influence on the composite outcome²⁸. In this study, the EWM was applied to calculate the weights w₁, w₂, and w₃ for THI, WEI, and ICL at each grid cell, as outlined below.

Let data_ij denote the value of indicator j (where j = 1,2,3) at grid cell i (where i = 1,2, …, n), with n denoting the number of samples. First, the data are normalized using the positive normalization method:

$$X_{\mathit{ij}}=\frac{{\mathit{data}}_{\mathit{ij}}-\min \left({\mathit{data}}_{ij}\right)}{\max \left({\mathit{data}}_{\mathit{ij}}\right)-\min \left({\mathit{data}}_{\mathit{ij}}\right)}(i=1,2,\cdots ,n;j=1,2,3)$$ 5

where X_ij is the normalized value.

subsequently, the proportion Y_ij of indicator j at grid cell i is computed as:

$$Y_{\mathit{ij}}=\frac{X_{\mathit{ij}}}{\sum _{i=1}^n{X}_{\mathit{ij}}}$$ 6

The entropy value E_j for indicator j is then calculated:

$$E_j=-\frac{1}{\ln n}\sum _{i=1}^n{(Y}_{\mathit{ij}}\times \ln Y_{\mathit{ij}})$$ 7

Finally, the entropy weight w_j for indicator j is determined as:

$$w_j=\frac{1-E_j}{\sum _{j=1}^3\left(1-E_j\right)}$$ 8

The composite climate comfort index based on EWM (CCI_EWM) is then given by:

$${\mathit{CCI}}_{\mathit{EWM}}=w_1\times \mathit{THI}+w_2\times \mathit{WEI}+w_3\times \mathit{ICL}$$ 9

where w₁, w₂, and w₃ are the EWM-derived weights for THI, WEI, and ICL, respectively. The THI, WEI, and ICL are calculated using Eqs. (1), (2), and (4), respectively.

Bayesian three-cornered hat (BTCH) Method

The BTCH method is employed to integrate multiple datasets without requiring prior knowledge, while accounting for cross-correlations among the datasets²³. It has been extensively applied for uncertainty assessment of geospatial products^29,30 and model outputs³¹. Meanwhile, the BTCH method, as an objective technique for data fusion, is extensively employed in data integration within geographical research²³.

In this study, the TCH method is initially used to assess the uncertainties of THI, WEI, and ICL, and then applied to weight these indices based on their posterior probabilities, resulting in the composite climate comfort index CCI_TCH.

First, the relative uncertainty σ_j (where j = 1,2,3) for each index is calculated, and the method reference to He et al.²³.

Next, the weights for each index are determined as:

$$w_k=\frac{{\prod }_{j=1,j\ne k}^3{\sigma }_j^2}{{\sum }_{k=1}^3\left({\prod }_{j=1,j\ne k}^3{\sigma }_j^2\right)}$$ 10

where k denotes the index type (with k=1,2,3 corresponding to THI, WEI, and ICL, respectively).

Finally, the composite index based on BTCH is computed as:

$${\mathit{CCI}}_{\mathit{TCH}}=w_1\times \mathit{THI}+w_2\times \mathit{WEI}+w_3\times \mathit{ICL}$$ 11

The implementation of BTCH in this study adheres the open-source code developed by Xu’s research team at Beijing Normal University²³.

Final composite index fusion

Based on the three composite indices—CCI_ESM, CCI_EWM, and CCI_TCH— obtained previously, the BTCH method (Eqs. (10, 11)) is applied to determine their weights w₁, w₂, and w₃, which are then used to fuse them into the final composite climate comfort index (CCI):

$$\mathit{CCI}=w_1\times {\mathit{CCI}}_{\mathit{ESM}}+w_2\times {\mathit{CCI}}_{\mathit{EWM}}+w_3\times {\mathit{CCI}}_{\mathit{TCH}}$$ 12

where CCI represents the final composite climate comfort index.

The overall methodological framework is depicted in Fig. 1, illustrating the sequential computation of the three base indices, the application of three weighting schemes, the assessment of uncertainties, and the final fusion into a single composite index.

Fig. 1.

Technical flowchart. (a) Computation of the three climate indices: THI, WEI, and ICL; (b) Derivation of three composite climate comfort indices—CCI_ESM, CCI_EWM, and CCI_TCH)—via the ESM, EWM, and BTCH weighting methods, respectively, and their subsequent integration into a unified CCI using the BTCH method.

Daily air temperature (°C), wind speed (m/s), relative humidity (%), and sunshine duration (hours) data spanning 1961–2022 were utilized to compute the Temperature-Humidity Index (THI), Wind Effect Index (WEI), and Clothing Index (ICL) across China. These calculations were performed at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, based on Eqs. (1, 2, 4), respectively. Then, these daily indices were then averaged to monthly values using the arithmetic mean. The values of these monthly indices were normalized into relative scores and classified into five distinct comfort levels: “very comfortable,” “comfortable,” “less uncomfortable,” “uncomfortable,” and “extremely uncomfortable.” Corresponding scores of 9, 7, 5, 3, and 1 were assigned to these levels, respectively. The comprehensive classification criteria and scoring scheme are detailed in Table 1.

Table 1.

Grading criteria for THI¹⁹, WEI¹⁴ and ICL¹⁶.

THI		WEI		ICL		Grading symbols	Sensory degree
Grading	Sensory degree	Grading	Sensory degree	Grading	Clothing level
<40	Extremely cold	<−1200	Extremely cold	>2.5	Down jacket or fur coat	1	Extremely uncomfortable
40~45	Cold	−1200~−1000	Cold	1.8~2.5	Casual clothes and thick coats	3	Uncomfortable
45~55	Slightly cold	−1000~−800	Slightly cold	1.5~1.8	Traditional winter clothing	5	Less uncomfortable
55~60	Cool	−800~−600	Cool	1.3~1.5	Common casual clothes in spring and autumn	7	Comfortable
60~65	Comfortable	−600~−300	Comfortable	0.7~1.3	Shirts and common casual clothes	9	Very comfortable
65~70	Warm	−300~−200	Warm	0.5~0.7	Light summer clothes	7	Comfortable
70~75	Slightly hot	−200~−50	Slightly hot	0.3~0.5	Short-sleeved and open-collar shirt	5	Less uncomfortable
75~80	Hot	−50~80	Hot	0.1~0.3	Tropical singlet	3	Uncomfortable
>80	Extremely hot	>80	Extremely hot	<0.1	Short skirt or shorts	1	Extremely uncomfortable

Under this classification framework, the composite climate comfort index (CCI) was defined as “comfortable” for scores ranging from 7 to 9, “less uncomfortable” for a score of 5, and “extremely uncomfortable” for scores between 1 and 3.

Data Records

The composite climate comfort index (CCI) dataset for China (1961–2022) is publicly available at the National Tibetan Plateau/Third Pole Environment Data Center³² under a CC-BY 4.0 license at https://doi.org/10.11888/Atmos.tpdc.303390. The dataset covers the entire land area of mainland China at a spatial resolution of 0.25° × 0.25° and spans monthly time steps from January 1961 to December 2022. All data are provided in geographic coordinates (WGS84) on a cell-center grid.

The final fused CCI dataset is available in two formats:

Final_Climateindex_TCH_Fusion_method.mat: a MATLAB-format file containing the variable Final_TCH_integ_index (lon × lat × time array), and Final_Climateindex_TCH_Fusion_method.xlsx: an Excel-format table.

In the table file, each row corresponds to a grid cell, with the first column encoding longitude (69.75°E to 140.25°E, spacing 0.25°), the second column encoding latitude (14.75°N to 55.25°N, spacing 0.25°), and the remaining 744 columns containing monthly CCI values. Column headers follow the YYYYMM format (e.g., 196101, 196102, …, 202212). The CCI values are dimensionless.

All grid cells outside the land domain of China are set to NaN. No other missing values exist within the valid spatial-temporal extent.

The individual CCI datasets generated by the three weighting schemes are provided in a compressed archive named Indices_data.zip, including Cindex.mat (variable Cindex): CCI_ESM from expert weighting, Entropyweightindex.mat (variable wight_index): CCI_EWM from entropy weighting, and TCH_integ_index.mat (variable TCH_integ_index): CCI_BTCH from BTCH fusion.

The corresponding grid-level uncertainty estimates are provided in uncertainty_data.zip, including TCH_data_uncertainty_Guding.mat (variable TCH_data_Guding): uncertainty of CCI_ESM, TCH_data_uncertainty_Entropy.mat (variable TCH_data_Entropy): uncertainty of CCI_EWM, and TCH_data_uncertainty_TCH.mat (variable TCH_data_TCH): uncertainty of CCI_BTCH.

All auxiliary files share the same spatial grid and temporal coverage as the primary dataset.

The continuous CCI values are classified into five levels: “extremely uncomfortable” (1), “uncomfortable” (3), “less uncomfortable” (5), “comfortable” (7), and “very comfortable” (9). Classification thresholds are not stored as a separate variable but can be applied by users following the criteria detailed in the Methods section.

Technical Validation

Spatial distribution of weighted climate comfort indices

Daily air temperature (°C), wind speed (m/s), relative humidity (%), and sunshine duration (hours) data spanning 1961–2022 were utilized to compute the Temperature-Humidity Index (THI), Wind Effect Index (K), and Clothing Index (ICL) across China. These calculations were performed at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, based on Eqs. (1, 2, 4), respectively.

To synthesize the three indices—ICL, WEI, and THI—into a composite climate comfort index, it was first necessary to determine the respective weights of each index. This study applied three established weighting methods: the expert scoring method (ESM), the Bayesian three-cornered hat (BTCH) method, and the entropy weight method (EWM).

Under the ESM, the weights assigned to ICL, WEI, and THI were 0.1, 0.3, and 0.6, respectively. As the ESM yields fixed weights that lack spatially variability, spatial distribution maps are not provided for this method.

The BTCH method produced weights for ICL, WEI, and THI that demonstrated pronounced spatial heterogeneity, with regional mean (median) values of 0.20 (0.14), 0.66 (0.75), and 0.14 (0.10), respectively. As illustrated in Fig. 2a, most regions exhibited ICL weights approximating 0.20, with elevated values (>0.80) over the Qiangtang Plateau of the Tibetan Plateau and values around 0.4 near Mohe Country in Heilongjiang Province. In contrast, the spatial distribution of WEI weights (Fig. 2b) displayed a pattern generally inverse to that of ICL, characterized by low values over the Tibetan Plateau and higher values elsewhere. THI weights were predominantly low (<0.20) across most areas, except for localized regions in the southeastern Tibetan Plateau where weights surpassed 0.60 (Fig. 2c).

Fig. 2.

Spatial distribution of weights obtained via the BTCH method and the entropy weight method (EWM) for the ICL, WEI, and THI indices. From top to bottom: ICL, WEI, and THI indices; left column shows BTCH weights, and right column shows EWM weights.

The EWM also yielded weights exhibiting considerable spatial variability, with regional mean (median) values of 0.48 (0.44), 0.12 (0.09), and 0.40 (0.41) for ICL, WEI, and THI, respectively. As depicted in Fig. 2d, high ICL weights were primarily concentrated in southern China, with low values prevailed in the north and on the Tibetan Plateau. WEI weights were generally low across most regions, except for the Qiangtang area on the Tibetan Plateau (Fig. 2e). The spatial distribution of THI weights (Fig. 2f) largely approximated the inverse pattern of WEI weights, featuring lower values in the south and higher values in the north.

In terms of regional averages, the weights derived from the EWM were more closely aligned with those from the ESM, whereas the weights obtained via the BTCH method exhibited more substantial deviations from the results of both the EWM and ESM.

Leveraging the grid-point weights derived from the ESM, EWM, and BTCH methodologies, three composite climate comfort indices—CCI_ESM, CCI_EWM, and CCI_BTCH—were formulated. The spatial patterns of climate comfort delineated by these indices exhibited broadly congruent trends (Fig. 3). Subsequently, the BTCH approach was employed to synthesize the three composite indices into a single, integrated composite climate comfort index, designated as CCI.

Fig. 3.

Composite climate comfort indices derived using three weighting methods. From top to bottom: January, April, July, and October; from left to right: CCI_ESM, CCI_EWM, and CCI_BTCH.

Uncertainty analysis

The BTCH method was employed to evaluate the uncertainties associated with three composite indices: CCI_EWM, CCI_BTCH, and CCI_ESM. As illustrated in Fig. 4a, the uncertainty of CCI_EWM is comparatively low across most of the northern China, northeast China, northwest China, and the Tibetan Plateau. Conversely, higher uncertainty is observed in regions south of the Yangtze River and within the Sichuan Basin. Across most areas, the uncertainty of CCI_EWM remains below 0.5 (Fig. 4b), with a mean value of 0.31 and a median of 0.14.

Fig. 4.

Uncertainty evaluation of the CCI_EWM (a,b), CCI_BTCH (c,d), and CCI_ESM (e,f) indices using the BTCH methodology. The left panels depict the spatial patterns of uncertainty, while the right panels display histograms of grid-point uncertainty values.

In contrast, the uncertainty of CCI_BTCH exhibits an opposing spatial pattern. Higher uncertainty values are evident in northern China, whereas lower values characterize southern China; the Tibetan Plateau also demonstrates low uncertainty (Fig. 4c). The mean uncertainty for the entire region is 0.62, with a median of 0.59 (Fig. 4d).

Regarding CCI_ESM, uncertainty is generally elevated across most regions, except for the Tibetan Plateau, where it remains relatively low (Fig. 4e). The mean uncertainty amounts to 0.67, accompanied by a median of 0.61 (Fig. 4f).

Leveraging the uncertainties of these three composite indices, a novel composite climate comfort index was formulated through data fusion. This process synthesizes the merits of each index to establish a more optimal, nationwide CCI.

Data fusion

Using the grid-point uncertainties associated with the CCI_EWM, CCI_BTCH, and CCI_ESM indices, the respective grid-point weights for each index were quantified. As illustrated in Fig. 5a, CCI_EWM exhibits relatively high weights across north China, northeast China, northwest China, and the Tibetan Plateau. In contrast, CCI_BTCH demonstrates elevated weights in the Sichuan Basin, southern China, and the southeastern Tibetan Plateau (Fig. 5b), whereas CCI_ESM is characterized by high weights solely in specific regions of the eastern Tibetan Plateau (Fig. 5c). Consequently, the composite climate comfort index, formulated through the integration of these three indices, synergizes their individual strengths, thereby yielding a more holistic depiction of climate comfort across China.

Fig. 5.

Spatial distribution of weights for the CCI_EWM (a), CCI_BTCH (b), and CCI_ESM (c) indices derived using the BTCH method.

Author contributions

Q.L. and L.L. conceptualized the comprehensive climate comfort index dataset. Q.L., in collaboration with Y.L., formulated the methodological framework and constructed the dataset. Q.L. and L.L. jointly conducted the result analysis. Q.L. undertook the data processing tasks. The manuscript was authored by Q.L.

Data availability

The composite climate comfort index (CCI) dataset, generated and analyzed in this study, is openly accessible via the National Tibetan Plateau/Third Pole Environment Data Center at https://doi.org/10.11888/Atmos.tpdc.303390.

Code availability

The custom code employed for model training and data generation is accessible at https://doi.org/10.11888/Atmos.tpdc.303390. This code was implemented and validated using MATLAB R2024a (https://github.com/QunLiu-fjnu/Comprehensive-Climate-Comfort-Index). The workflow corresponding to these procedures is comprehensively outlined in the Methods section.

Competing interests

The authors declare no competing interests.