Climate change will make increasing people less comfortable: Insights from the weather preference index

Climatic comfort, defined as the state in which individuals perceive their surrounding climate as comfortable without artificial heating or cooling (SAC, 2012), is a critical determinant of human well-being and health (Rappaport, 2007; Egan and Mullin, 2016). In recent decades, climate change has profoundly altered key climatic factors such as temperature, humidity, and precipitation, directly affecting human climatic comfort worldwide (Guerreiro et al., 2018; Fang et al., 2020; Gholami et al., 2025). Numerous studies have demonstrated that increasing global temperatures, more frequent extreme heat events, and changing precipitation patterns have already led to significant declines in human climatic comfort (Tuholske et al., 2021; Wang et al., 2024, 2025). For instance, Ren et al. (2022) found that from 1990 to 2016, urban residents in China experienced increasingly severe summer heat stress and discomfort, largely driven by climate change. Similarly, Grant et al., 2025 projected that under a 3.5 °C warming scenario, 92 % of people born in 2020 will face unprecedented lifetime exposure to extreme heat, underscoring the escalating threat of climatic discomfort worldwide. Given these documented and projected impacts, it is essential to quantitatively assess the impacts of future climate change on global climatic comfort (Lenton et al., 2023; Wang et al., 2023a; Sun et al., 2024). Such assessments are not only vital for identifying populations and regions at heightened risk but also for informing urban planning, public health strategies, and climate-resilient infrastructure design, helping to support the development of regionally differentiated adaptation and mitigation measures, thereby enhancing the resilience of human settlements in the face of a changing climate.

Currently, several studies have investigated the impacts of climate change on climatic comfort. On a global scale, Xu et al. (2020) analyzed the impacts of global climate change on climatic comfort under the SSP3-8.5 scenario, utilizing the Human Climate Niche indicator. At the national level, Huang et al. (2020) evaluated the influence of climate change on climatic comfort in the United States for the years 1998, 2050, and 2080, based on the Universal Thermal Climate Index. At the local scale, Ren et al. (2022) assessed the impacts of climate change on climatic comfort across 183 Chinese cities from 1990 to 2016, using the Physiological Equivalent Temperature indicator. The evaluation indicators employed in these studies can primarily be categorized into two types: empirical indicators (e.g., the Human Climate Niche indicator or the Wet Bulb Globe Temperature) and mechanistic indicators (e.g., the Universal Thermal Climate Index or the Physiological Equivalent Temperature) (Li et al., 2020; Zhao et al., 2021; Tan et al., 2022). However, both types of indicators exhibit limitations. Empirical indicators are typically constructed based on data from specific regions or environmental settings, thus lacking generalizability and potentially being difficult to apply to other areas or settings (Potchter et al., 2018). In contrast, mechanistic indicators, while grounded in solid theoretical principles, involve complex computational processes and often face challenges in obtaining the required data. Consequently, when assessing the impacts of global climate change on climatic comfort under various future scenarios, both types of indicators may encounter significant challenges.

The Weather Preference Index (WPI) provides a straightforward and reliable method for comprehensively evaluating the impacts of future climate change on global climatic comfort. Introduced by Egan and Mullin (2016), the WPI aims to systematically assess the effects of climate change on climatic comfort. It is based on a century of historical population data and meteorological records, and uses multiple regression analysis to show the relationships between variations in meteorological factors and population size (Rappaport, 2007). The distinctiveness of the WPI lies in its universality: it considers meteorological conditions during both summer and winter, serving as a climate-neutral assessment metric applicable to various climatic regions. Additionally, its calculation method is relatively straightforward, and the required data are relatively easy to obtain, making the WPI an ideal indicator for assessing the impacts of future climate change on climatic comfort. Most importantly, the WPI reflects humanity’s long-term climate preferences, delineating a relatively suitable climate range for humans (Egan and Mullin, 2016). More detailed information on the distinctiveness of the WPI compared with both empirical and mechanistic indicators is provided in Note S1 and Table S1 within the Supplementary material. In recent years, several studies have successfully applied the WPI in regional-scale assessments of the impacts of future climate change on climatic comfort. For instance, Egan and Mullin (2016) used the WPI to project the impacts of climate change on climatic comfort in the United States from 2025 to 2099. Similarly, Fang et al. (2020) applied the WPI to reveal the potential challenges China may face regarding climatic comfort in the coming decades (2025–2100). These empirical investigations further validate the effectiveness and practicality of the WPI in assessing the impacts of climate change on climatic comfort.

This study aims to utilize the WPI for a comprehensive assessment of the impacts of future climate change on global climatic comfort. First, we quantified the historical variation characteristics of the WPI globally for the period 1980–2019 using meteorological observation data. Second, using future climate model data, we analyzed the change trends of the WPI globally from 2020 to 2100. Subsequently, we classified different change zones of climatic comfort according to the rates of historical and future WPI changes. Building upon this, we examined the exposure of the total, urban, and rural populations to these different change zones of climatic comfort. The findings of this research can provide robust data support and scientific insights for formulating targeted and actionable climate change adaptation and mitigation strategies in diverse regions globally, thereby mitigating the adverse effects of climate change and contributing to the advancement of global sustainable development.