气候变化背景下中国新疆与中亚五国旅游气候舒适度时空演变分析

doi:10.12118/j.issn.1000-6060.2025.590

Abstract

Abstract:

As a typical arid region and a key corridor along the Silk Road tourism route, Xinjiang, China, and the five Central Asian countries require a systematic assessment of the spatiotemporal variability of tourism climate comfort and its future risks. Based on high-resolution gridded meteorological data and CMIP6 multimodel simulations, this study systematically analyzes the spatiotemporal evolution of the holiday climate index (HCI) from 1960 to 2100. The results indicate the following: (1) The HCI exhibits an overall increasing trend, with notable regional and seasonal variations. Greater increases occur in mountainous areas, whereas some arid basins exhibit slight decreases. Under the SSP585 scenario, both the magnitude of future HCI increases and associated uncertainties are higher than those under the SSP245 scenario. (2) Intra-annual variations of HCI exhibit “single-peak” and “double-peak” patterns. Future warming is projected to reduce summer comfort in some regions, with summer HCI under the high-emission SSP585 scenario in the late future (2071—2100) falling below the historical baseline (1985—2014), thereby narrowing the suitable tourism window. (3) The spatial distribution of HCI shows clear latitudinal zonality and vertical differentiation, with high-value areas concentrated in low-altitude, low-latitude regions. Although these high-value areas are projected to expand further in the future, extremely high summer temperatures are expected to reduce the extent of the highest-comfort zones (HCI>90). (4) The tourism climate comfort period has significantly lengthened, with greater increases under high-emission scenarios, though accompanied by enhanced variability. Spatially, comfort periods are longer in low-latitude, low-altitude regions and shorter in high-latitude, high-altitude areas, with significant increases projected in high-altitude mountainous regions over the long term. These findings provide a scientific basis for climate adaptation and the sustainable development of the tourism sector in the region.

Key words: HCI, tourism climate comfort, climate change scenarios, spatiotemporal evolution, climate risk

GUAN Jingyun, SUN Zhiyang, XU Xiaoliang, CHENG Yifeng, SONG Beibei, FANG Ruiying. Spatiotemporal evolution of tourism climate comfort in Xinjiang, China and five Central Asian countries under climate change[J].Arid Land Geography, 2026, 49(4): 697-712.

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References 49

[1]	宋瑞, 保继刚, 魏小安, 等. 旅游强国建设与旅游业高质量发展[J]. 旅游学刊, 2024, 39(7): 16-27.
	[Song Rui, Bao Jigang, Wei Xiaoan, et al. Construction of the strong tourism country and high-quality development of tourism[J]. Tourism Tribune, 2024, 39(7): 16-27.]
[2]	World Travel & Tourism Council. Economic impact research 2025[R]. London: World Travel & Tourism Council, 2025.
[3]	Dogru T, Marchio E A, Bulut U, et al. Climate change: Vulnerability and resilience of tourism and the entire economy[J]. Tourism Management, 2019, 72: 292-305. doi: 10.1016/j.tourman.2018.12.010
[4]	Martín M B G. Weather, climate and tourism a geographical perspective[J]. Annals of Tourism Research, 2005, 32(3): 571-591. doi: 10.1016/j.annals.2004.08.004
[5]	贾妮娅·叶力肯, 侯建楠, 刘思博. 近30 a新疆地州市旅游气候舒适度时空特征分析[J]. 干旱区地理, 2025, 48(2): 212-222. doi: 10.12118/j.issn.1000-6060.2024.086
	[Yeliken Jianiya, Hou Jiannan, Liu Sibo. Spatio-temporal characterization of tourism climate comfort in Xinjiang prefectures and cities in the last 30 years[J]. Arid Land Geography, 2025, 48(2): 212-222.] doi: 10.12118/j.issn.1000-6060.2024.086
[6]	Gössling S, Scott D, Hall C M, et al. Consumer behaviour and demand response of tourists to climate change[J]. Annals of Tourism Research, 2012, 39(1): 36-58. doi: 10.1016/j.annals.2011.11.002
[7]	Rutty M, Hewer M, Knowles N, et al. Tourism & climate change in North America: Regional state of knowledge[J]. Journal of Sustainable Tourism, 2024, 32(9): 1924-1947. doi: 10.1080/09669582.2022.2127742
[8]	IPCC. Climate change 2021: The physical science basis[M]. Cambridge: Cambridge University Press, 2023.
[9]	Scott D. Tourism and the climate crisis[J]. Journal of Sustainable Tourism, 2024, 32(9): 1709-1724. doi: 10.1080/09669582.2024.2391911
[10]	关靖云, 李东, 徐晓亮, 等. 近40年新疆旅游气候舒适期的时空格局及其演变研究[J]. 西南大学学报(自然科学版), 2022, 44(6): 185-197.
	[Guan Jingyun, Li Dong, Xu Xiaoliang, et al. Spatialtemporal pattern and evolution of tourism climate comfort period in Xinjiang in recent 40 years[J]. Journal of Southwest University (Natural Science Edition), 2022, 44(6): 185-197.]
[11]	Fallah B, Didovets I, Rostami M, et al. Climate change impacts on Central Asia: Trends, extremes and future projections[J]. International Journal of Climatology, 2024, 44(10): 3191-3213. doi: 10.1002/joc.v44.10
[12]	Qiu Y, Feng J M, Yan Z W, et al. High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs[J]. Climate Dynamics, 2022, 58(3): 777-791. doi: 10.1007/s00382-021-05934-2
[13]	陈熠瑶, 杨丽敏, 王玮. 合作之路越走越宽中国中亚旅游“双向奔赴”[N]. 中国旅游报, 2025-06-20(001).
	[Chen Yiyao, Yang Limin, Wang Wei. The road of cooperation is wider and wider: China-central tourism’s “Two-way Journey”[N]. China Tourism News, 2025-06-20(001).]
[14]	邓浩. 中国对中亚外交: 进展、经验与未来方向[J]. 国际问题研究, 2022(4): 100-116, 141-142.
	[Deng Hao. China’s diplomacy in Central Asia: Progress, experience and future directions[J]. International Studies, 2022(4): 100-116, 141-142.]
[15]	曾向红, 王子寒. “三大倡议”与新时期中国的中亚外交[J]. 国际展望, 2024, 16(1): 75-96, 160.
	[Zeng Xianghong, Wang Zihan. The “Three Major Initiatives” and China’s Central Asia diplomacy in the New Era[J]. Global Review, 2024, 16(1): 75-96, 160.]
[16]	孙根年, 马丽君. 西安旅游气候舒适度与客流量年内变化相关性分析[J]. 旅游学刊, 2007, 22(7): 34-39.
	[Sun Gennian, Ma Lijun. An analysis of tourist climate comfortable degree and yearly variation of tourist traffic in Xi’an[J]. Tourism Tribune, 2007, 22(7): 34-39.]
[17]	朱妮娜, 曾刚, 史正燕, 等. 旅游气候舒适度的模型改进及时空演化[J]. 旅游科学, 2023, 37(4): 144-160.
	[Zhu Nina, Zeng Gang, Shi Zhengyan, et al. Model modification and spatiotemporal evolution of the comfortability degree of tourism climate[J]. Tourism Science, 2023, 37(4): 144-160.]
[18]	Zhao J Y, Wang S J. Spatio-temporal evolution and prediction of tourism comprehensive climate comfort in Henan Province, China[J]. Atmosphere, 2021, 12(7): 823, doi: 10.3390/atmos12070823.
[19]	Mieczkowski Z. The tourism climatic index: A method of evaluating world climates for Tourism[J]. Canadian Geographer/Le Géographe Canadien, 1985, 29(3): 220-233. doi: 10.1111/cag.1985.29.issue-3
[20]	Höppe P. The physiological equivalent temperature: A universal index for the biometeorological assessment of the thermal environment[J]. International Journal of Biometeorology, 1999, 43(2): 71-75. pmid: 10552310
[21]	Blazejczyk K, Epstein Y, Jendritzky G, et al. Comparison of UTCI to selected thermal indices[J]. International Journal of Biometeorology, 2012, 56(3): 515-535. doi: 10.1007/s00484-011-0453-2 pmid: 21614619
[22]	马丽君, 孙根年, 王洁洁. 中国东部沿海沿边城市旅游气候舒适度评价[J]. 地理科学进展, 2009, 28(5): 713-722.
	[Ma Lijun, Sun Gennian, Wang Jiejie. Evaluation of tourism climate comfortableness of coastal cities in the eastern China[J]. Progress in Geography, 2009, 28(5): 713-722.] doi: 10.11820/dlkxjz.2009.05.009
[23]	Rutty M, Scott D. Thermal range of coastal tourism resort microclimates[J]. Tourism Geographies, 2014, 16(3): 346-363. doi: 10.1080/14616688.2014.932833
[24]	De Freitas C R, Scott D, McBoyle G. A second generation climate index for tourism (CIT): Specification and verification[J]. International Journal of Biometeorology, 2008, 52(5): 399-407. doi: 10.1007/s00484-007-0134-3 pmid: 18097690
[25]	Tartarini F, Schiavon S. Pythermal comfort: A Python package for thermal comfort research[J]. SoftwareX, 2020, 12: 100578, doi: 10.1016/j.softx.2020.100578.
[26]	孙美淑, 李山. 气候舒适度评价的经验模型: 回顾与展望[J]. 旅游学刊, 2015, 30(12): 19-34.
	[Sun Meishu, Li Shan. Empirical indices evaluating climate comfortableness: Review and prospect[J]. Tourism Tribune, 2015, 30(12): 19-34.]
[27]	蔚丹丹, 李山, 张粮锋, 等. 旅游气候舒适性评价: 模型优化与中国案例[J]. 旅游学刊, 2021, 36(5): 14-28.
	[Wei Dandan, Li Shan, Zhang Liangfeng, et al. Evaluate tourism climate using modified holiday climate index in China[J]. Tourism Tribune, 2021, 36(5): 14-28.]
[28]	Scott D, Rutty M, Amelung B, et al. An inter-comparison of the holiday climate index (HCI) and the tourism climate index (TCI) in Europe[J]. Atmosphere, 2016, 7(6): 80, doi: 10.3390/atmos7060080.
[29]	Kholmatjanov B M, Abdulakhatov E I, Begmatov S U, et al. Bioclimatic conditions of the classic tourist route Tashkent-Samarkand-Bukhara-Khiva in Uzbekistan[J]. WSEAS Transactions on Environment and Development, 2023, 19: 1255-1275. doi: 10.37394/232015
[30]	Guan J Y, Yao J Q, Li M Y, et al. Historical changes and projected trends of extreme climate events in Xinjiang, China[J]. Climate Dynamics, 2022, 59(5): 1753-1774. doi: 10.1007/s00382-021-06067-2
[31]	周悦, 鲁瑞洁, 黄梦真. 伊犁河流域多时间尺度气象干旱时空特征[J/OL]. 干旱区地理. [2025-10-10]. https://link.cnki.net/urlid/65.1103.X.202509-30.1434.002.
	[Zhou Yue, Lu Ruijie, Huang Mengzhen. Spatiotemporal characteristics of multi-scale meteorological drought in the Ili River Basin[J/OL]. Arid Land Geography. [2025-10-10]. https://link.cnki.net/urlid/65.1103.X.202509-30.1434.002. ]
[32]	Yan Y C, Xu Y Y, Yue S P. A high-spatial-resolution dataset of human thermal stress indices over South and East Asia[J]. Scientific Data, 2021, 8(1): 229, doi: 10.1038/s41597-021-01010-w. pmid: 34471140
[33]	蒋文好, 陈活泼. CMIP6模式对亚洲中高纬区极端温度变化的模拟及预估[J]. 大气科学学报, 2021, 44(4): 592-603.
	[Jiang Wenhao, Chen Huopo. Assessment and projection of changes in temperature extremes over the mid-high latitudes of Asia based on CMIP6 models[J]. Transactions Atmospheric Sciences, 2021, 44(4): 592-603.]
[34]	古再丽努尔·亚森, 张京朋, 赵天保. CMIP6多模式对21世纪中亚极端降水未来变化预估[J]. 气候与环境研究, 2023, 28(3): 286-302.
	[Yasen Guzailinuer, Zhang Jingpeng, Zhao Tianbao. CMIP6 model-projected future changes in extreme precipitation over Central Asia in the 21^st century[J]. Climate and Environment Research, 2023, 28(3): 286-302.]
[35]	Yao J Q, Chen Y N, Chen J, et al. Intensification of extreme precipitation in arid Central Asia[J]. Journal of Hydrology, 2021, 598: 125760, doi: 10.1016/j.jhydrol.2020.125760.
[36]	Peng D D, Zhou T J, Zhang L X, et al. Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 ℃ less global warming[J]. Climate Dynamics, 2020, 54(1-2): 543-560. doi: 10.1007/s00382-019-05014-6
[37]	Yu D D, Matthews L, Scott D, et al. Climate suitability for tourism in China in an era of climate change: A multiscale analysis using holiday climate index[J]. Current Issues in Tourism, 2022, 25(14): 2269-2284. doi: 10.1080/13683500.2021.1956442
[38]	Matthews L, Scott D, Andrey J. Development of a data-driven weather index for beach parks tourism[J]. International Journal of Biometeorology, 2021, 65(5): 749-762. doi: 10.1007/s00484-019-01799-7
[39]	Sen P K. Estimates of the regression coefficient based on Kendall’s Tau[J]. Journal of the American Statistical Association, 1968, 63(324): 1379-1389. doi: 10.1080/01621459.1968.10480934
[40]	Mann H B. Non-parametric test against trend[J]. Econometrica, 1945, 13(3): 245-259. doi: 10.2307/1907187
[41]	闫昕旸, 张强, 张文波, 等. 泛中亚干旱区气候变化特征分析[J]. 干旱区研究, 2021, 38(1): 1-11. doi: 10.13866/j.azr.2021.01.01
	[Yan Xinyang, Zhang Qiang, Zhang Wenbo, et al. Analysis of climate characteristics in the Pan-Central-Asia arid region[J]. Arid Zone Research, 2021, 38(1): 1-11.] doi: 10.13866/j.azr.2021.01.01
[42]	阿帕尔·肉孜, 叶尔克江·霍依哈孜, 高培, 等. 新疆近50 a气候的变化特征[J]. 南京信息工程大学学报(自然科学版), 2014, 6(4): 356-362.
	[Rouzi Apaer, Huoyihazi Yeerkejiang, Gao Pei, et al. Climatic change characteristics of Xinjiang during recent 50 years[J]. Journal of Nanjing University of Information Science and Technology: Natural Science Edition, 2014, 6(4): 356-362.]
[43]	Mountain Research Initiative Edw Working Group. Elevation-dependent warming in mountain regions of the world[J]. Nature Climate Change, 2015, 5(5): 424-430. doi: 10.1038/nclimate2563
[44]	周波涛, 钱进. IPCC AR6报告解读: 极端天气气候事件变化[J]. 气候变化研究进展, 2021, 17(6): 713-718.
	[Zhou Botao, Qian Jin. Changes of weather and climate extremes in the IPCC AR6[J]. Climate Change Research, 2021, 17(6): 713-718.]
[45]	Becken S, Wilson J. The impacts of weather on tourist travel[J]. Tourism Geographies, 2013, 15(4): 620-639. doi: 10.1080/14616688.2012.762541
[46]	Li R, Chi X L. Thermal comfort and tourism climate changes in the Qinghai-Tibet Plateau in the last 50 years[J]. Theoretical and Applied Climatology, 2014, 117(3): 613-624. doi: 10.1007/s00704-013-1027-5
[47]	IPCC. Climate Change 2022: Impacts, adaptation and vulnerability[M]. Cambridge: Cambridge University Press, 2023.
[48]	Gossling S, Balas M, Mayer M, et al. A review of tourism and climate change mitigation: The scales, scopes, stakeholders and strategies of carbon management[J]. Tourism Management, 2023, 95: 104681, doi: 10.1016/j.tourman.2022.104681.
[49]	陈亚宁, 李稚, 方功焕, 等. 气候变化对中亚天山山区水资源影响研究[J]. 地理学报, 2017, 72(1): 18-26. doi: 10.11821/dlxb201701002
	[Chen Yaning, Li Zhi, Fang Gonghuan, et al. Impact of climate change on water resources in the Tianshan Mountains, Central Asia[J]. Acta Geographica Sinica, 2017, 72(1): 18-26.] doi: 10.11821/dlxb201701002

模式名称	所属国家（地区）	模式所属机构	分辨率（纬度×经度）
ACCESS-CM2	澳大利亚	CSIRO	1.25°×1.875°
ACCESS-ESM1-5	澳大利亚	CSIRO	1.25°×1.875°
BCC-CSM2	中国	BCC	1.125°×1.125°
CanESM5	加拿大	CCCma	2.8°×2.8°
INM-CM5-0	俄罗斯	INM RAS	2.0°×1.5°
IPSL-CM6A-LR	法国	IPSL	1.25°×2.5°
NorESM2-LM	挪威	NCC	2.5°×1.9°
NorESM2-MM	挪威	NCC	1.25°×0.9°

等级分类	有效温度/℃	云量覆盖率/%	风速/km·h^-1	日降水量/mm
10	23~25	11~20	1~9	0.00
9	20~22、26	1~10、21~30	10~19	0.01~2.99
8	27~28	0、31~40	0、20~29	3.00~5.99
7	18~19、29~30	41~50	-	-
6	15~17、31~32	51~60	30~39	-
5	11~14、33~34	61~70	-	6.00~8.99
4	7~10、35~36	71~80	-	-
3	0~6	81~90	40~49	-
2	-5~-1、37~39	90~99	-	9.00~12.00
1	≤-6	100	-	-
0	≥39	-	50~70	12.01~25.00
-1	-	-	-	>25.00
-10			>70

HCI分值区间	评分描述
90~100	理想
80~89	优异
70~79	优良
60~69	良好
50~59	可接受
30~49	临界
10~29	难以接受
0~9	危险

Spatiotemporal evolution of tourism climate comfort in Xinjiang, China and five Central Asian countries under climate change

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