Anthropogenic climate change drives rising global heat stress and its spatial inequality

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: major_revision

Abstract
Global heat stress is intensifying under climate change, yet the relative roles of natural and anthropogenic forcing remain insufficiently quantified. Here, we show that global heat stress trend, assessed with the Universal Thermal Climate Index, increases markedly over the past four decades, with 52% of land area experiencing rises in mean heat stress intensity and 67% showing increases in extreme heat stress days. We find that anthropogenic climate change overwhelmingly dominates these trends, with the land area it dominates nearly twice as large as that dominated by natural climate change. Anthropogenic climate change also results in pronounced spatial inequality in heat stress trends across different economies, with low-income economies experiencing a growth rate two to three times higher than that of high-income economies. These findings demonstrate that human-induced climate change is amplifying global heat stress while deepening existing spatial inequalities, underscoring the urgency of equitable climate change adaptation.

Introduction
Global heat stress is escalating, with extreme high temperatures and prolonged heat waves becoming increasingly frequent, intense, and persistent, resulting in severe consequences for human health and socioeconomic stability1,2. This increase in heat stress is largely driven by climate change, which is intensifying climatic extremes across a growing number of regions, particularly within tropical and subtropical climates3. In these regions, the combined effects of high temperatures and high humidity are pushing ambient heat levels dangerously close to human survival thresholds4. Additionally, evidence highlights the growing unevenness in how climate change impacts heat stress, disproportionately burdening low- and middle-income economies, exacerbating international disparities, and intensifying global environmental injustice5. As global temperatures rise, inequalities in heat stress exposure are projected to widen, necessitating immediate and informed interventions to ensure equitable climate change adaptation. Comprehensive assessments of heat stress under climate change provide the theoretical and practical foundations necessary for effective mitigation, which are also essential for informing policies aimed at safeguarding vulnerable populations and promoting environmental justice.

Recent studies have increasingly emphasized the importance of moving beyond conventional temperature-based analyses to evaluate human thermal comfort more comprehensively6,7. Metrics such as the Universal Thermal Climate Index (UTCI), wet-bulb temperature, and net effective temperature have emerged as critical tools for capturing complex interactions between temperature, humidity, radiation, and wind, providing a more accurate assessment of thermal comfort and human health8,9. Despite these advancements, critical research gaps remain. In particular, existing studies have predominantly investigated heat stress using simple temperature-based indices10,11, with limited exploration of historical variability and spatial inequality using human-perceived thermal stress indicators. Furthermore, although there is a broad consensus on climate change’s general role in intensifying heat stress, the precise contributions and relative importance of anthropogenic versus natural climate change remain inadequately quantified, particularly in terms of their differential impacts across socioeconomically diverse regions2.

Recent literature has underscored that heat stress impacts are intricately linked with socioeconomic vulnerabilities5, and has stressed the need to evaluate spatial inequality in heat stress trend across economies by considering both anthropogenic and natural climate change12. Studies have indicated pronounced disparities in adaptive capacities and vulnerability to heat stress, particularly in South Asia, Africa, and South America, where extreme heat events have increasingly threatened economic stability and public health13. Thus, a comprehensive global assessment of heat stress trends, their climatic drivers, and associated spatial inequality across economies is essential for formulating targeted mitigation strategies and effective adaptation policies.

Addressing these research gaps requires an examination in three key issues: (1) detailed characterization of the spatial and temporal dynamics of global heat stress using indicators reflective of human thermal comfort; (2) rigorous attribution of heat stress trend to anthropogenic and natural climatic change; and (3) an explicit analysis of spatial inequality in heat stress trend across economies differing in socioeconomic development. Addressing these issues will advance the understanding of global heat stress patterns, inform policy-making targeted at reducing spatial inequality, and strengthen adaptive capacity where it is most urgently needed.

In this study, we employ the UTCI to quantify global heat stress trends from 1981 to 2020. Using statistical and machine-learning approaches, including the Theil-Sen estimator, Mann–Kendall trend analysis, and the Light Gradient Boosting Machine (LightGBM), we attribute heat stress trend to anthropogenic and natural climate drivers. We further assess how these contributions vary across economies classified by the World Bank as high-income (H), upper-middle-income (UM), lower-middle-income (LM), and low-income (L) groups14. Together, this framework identifies the distinct roles of anthropogenic and natural climate change in shaping global heat stress intensification and quantifies the extent to which these drivers differentially affect economies at varying levels of socioeconomic development, thereby informing assessments of climate-related environmental justice.

Results
Global mean heat stress intensity and extreme heat stress days
We quantified the mean heat stress intensity and extreme heat stress days using UTCI data, applying the Theil–Sen estimator combined with the Mann–Kendall trend test to calculate global heat stress trend from 1981 to 2020 (Table 1). We also compared the differences in trends for each two-decade period to identify land area and regions where heat stress increased (Figs. 1 and 2). Regions were defined using the IPCC AR6 reference land regions (Supplementary Fig. 1). The results showed that the border areas of Eurasia and North Africa exhibited a higher increasing trend in mean heat stress intensity (Fig. 1a). Approximately 52% of global land area showed a significant increase in mean heat stress intensity (Fig. 1b), with an overall trend of 0.012°C·yr−1 (Fig. 1c). Notably, the annual trend in mean heat stress intensity increased over time, with a trend of 0.017°C·yr−1 from 2001 to 2020, more than twice the trend from the preceding period of 1981–2000 (Table 1). A comparative analysis of regional trends over time (Fig. 1d and Supplementary Table 1) revealed that 37 regions experienced a higher pace of increase during 2001–2020 compared to 1981–2000. Eastern Australia and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trend increases for 2001–2020 of 0.082°C·yr−1 and 0.071°C·yr−1 relative to 1981–2000, respectively. Comparable surges were evident in western Southern Africa (trend rising from 0 to 0.031°C·yr−1) and eastern Southern Africa (from −0.006 to 0.021°C·yr−1). In north-eastern South America, the trend accelerated from 0.011 to 0.030°C·yr−1, and in north-western South America, it increased from 0.001 to 0.021°C·yr−1, underscoring that Africa and South America were experiencing increases on par with those in Australia.

The increasing trend of extreme heat stress days was notably higher in the land area between the 30° north and south latitudes (Fig. 2a). Approximately 67% of global land area showed a significant increasing trend in extreme heat stress days (Fig. 2b), with a trend of about 0.429 days·yr−1 (Fig. 2c). Moreover, the annual trend in extreme heat stress days increased over time, with a trend of 0.772 days·yr−1 from 2001 to 2020, nearly three times the trend observed between 1981 and 2000 (Table 1). A comparison of regional trends over time (Fig. 2d and Supplementary Table 2) revealed that 40 regions experienced a higher pace of increase in the latter period. Northern Australia experienced the largest increase, with the trend increase of 2.248 days·yr−1 for 2001–2020 relative to 1981–2000. The low-latitude tropics recorded similarly steep rises. For example, Central Africa reached +2.029 days·yr−1, with North-Eastern Africa +1.494 days·yr−1, and Madagascar +1.444 days·yr−1. Similarly, pronounced surges were observed across tropical South America. The North South America sub-region showed an increment of +1.584 days·yr−1, and the South American Monsoon zone rose by +1.334 days·yr−1, with the North-Eastern South America by +1.323 days·yr−1. These values confirmed that the rapid escalation in extreme heat stress days was also occurring across equatorial Africa as well as tropical South America. Additionally, the increasing trend in extreme heat stress days globally was more pronounced than that of the trend in mean heat stress intensity, suggesting that future climate change may lead to disproportionately severe impacts from extreme heat.

Impacts of natural and anthropogenic climate change on global heat stress trend
We employed LightGBM to investigate the relationship between climate change and heat stress trend. Then, using counterfactual analysis, we constructed three scenarios of historical scenario (ALL), natural-only scenario (NAT), and anthropogenic-only scenario (Anthro), to quantify the relative contributions of natural and anthropogenic climate change to heat stress trend, thereby distinguishing their effects.

Increasing temperatures were the primary factor affecting heat stress trend, with temperature trends explaining 28% and 35% of the variation in global mean heat stress intensity and extreme heat stress days, respectively. Furthermore, the impact of anthropogenic climate change on heat stress trend was found to be much greater than that of natural climate change (Fig. 3). For the period 1981–2000, the trends attributable to anthropogenic climate change were 0.012°C·yr⁻¹ for mean intensity and 0.267 days·yr⁻¹ for extreme heat stress days. These values exceeded the trends due to natural climate change by 0.017°C·yr⁻¹ and 0.302 days·yr⁻¹, respectively. From 2001 to 2020, the anthropogenic trends increased to 0.015°C·yr⁻¹ and 0.491 days·yr⁻¹, exceeding the natural trends by 0.014°C·yr⁻¹ and 0.316 days·yr⁻¹. Regions such as Northeastern South America, Central Africa, and Northern Australia experienced both higher mean heat stress intensity and extreme heat stress days under anthropogenic climate change (Fig. 3).

Moreover, the increases in global heat stress were predominantly driven by anthropogenic climate change, while regions with decreasing heat stress trends were largely influenced by natural climate change (Fig. 4). Notably, the geographical area experiencing increases driven by anthropogenic climate change was approximately twice as large as the area affected by natural climate change and was predominantly located between 30° north and south latitude. Within this latitudinal band, regions such as Brazil and many parts of equatorial Africa showed a particularly strong anthropogenic signal. Conversely, areas dominated by natural climate change-related decreases in heat stress were about two to seven times larger than those affected by anthropogenic climate change, which were mostly situated at high latitudes, such as northern North America and the Russian Arctic.

Spatial inequality in global heat stress trend across economies
To investigate the spatial inequality in global heat stress trend, we analyzed the differences in heat stress trend among economies with varying levels of socioeconomic development (Fig. 5a), under three scenarios (ALL, NAT, and Anthro). This was conducted at the economy level using the concentration index to quantify spatial inequality.

Heat stress trend showed divergent patterns across economies, dependent on their level of socioeconomic development. Under Anthro scenario, low-income and lower-middle-income economies experienced higher trends in the increase of heat stress compared to high-income and upper-middle-income economies. Specifically, low-income economies showed increases approximately two to three times greater than those in high-income economies (Fig. 5b–e). Furthermore, the concentration index for heat stress trend under the Anthro scenario was positive, whereas that under NAT scenario was negative (Fig. 5f, g). These results indicated that anthropogenic climate change was the primary driver of spatial inequality in heat stress trend across economies. In contrast, natural climate change tended to partially offset this spatial inequality.

Moreover, under Anthro scenario, spatial inequality in heat stress trend across economies had intensified between 2001 and 2020, and spatial inequality across economies in extreme heat stress days was higher than that in mean heat stress intensity, with the concentration index for extreme heat stress days being about two to four times that of mean heat stress intensity. Under ALL scenario, which combined both natural and anthropogenic climate change, mean heat stress intensity trend did not show significant spatial inequality at the global economy level. By contrast, extreme heat stress days trend revealed substantial spatial inequality across economies. This contrast indicates that extreme heat stress days have more adverse impacts on low-income economies than changes in mean heat stress intensity.

Methods
To quantify and attribute global heat stress trend, we implemented a five-step workflow. Firstly, we extracted daily maximum UTCI values from the ERA5-HEAT reanalysis (1981–2020) to compute yearly mean heat stress intensity and extreme heat stress days. Secondly, we quantified temporal trends using the robust Theil-Sen slope estimator paired with the Mann–Kendall non-parametric test to assess statistical significance. Thirdly, we trained a LightGBM model, chosen for its speed, scalability, and high accuracy in nonlinear regression, to link local trends in temperature and humidity, as well as geographical coordinates to observed heat stress trend. Fourthly, we disentangled anthropogenic from natural climate change by constructing NAT and Anthro scenarios using counterfactual analysis. Finally, we assessed spatial inequality in heat stress trend across economies by ranking economies according to 2020 GDP per capita and computing concentration index under ALL, NAT, and Anthro scenarios.

Data preprocessing
The UTCI has been widely used to quantify changes in heat stress and human heat exposure6,40. UTCI is a thermophysiological index designed to reflect human thermal comfort by incorporating air temperature, humidity, wind, and radiation into a biophysical heat-exchange model calibrated on human physiology41,42. ERA5-HEAT is the first set of global historical UTCI raster data, which is based on ERA5 meteorological reanalysis data43, and offers a temporal resolution of hour-by-hour and a spatial resolution of 0.25°44. We selected 40 years of ERA5-HEAT data, spanning 1981 to 2020. From this dataset, we extracted daily maximum UTCI values from the hour-by-hour data to characterize heat stress.

Meteorological data were obtained from the ERA5-Land and ERA5 reanalysis datasets produced by the European Centre for Medium-Range Weather Forecasts. We selected temperature and relative humidity data from the period 1981–2020, both with an hour-by-hour temporal resolution. Temperature data were sourced from the ERA5-Land dataset, which has a spatial resolution of 0.1°. From this dataset, daily mean temperature values were extracted to calculate annual mean temperatures, which were then resampled to a 0.25° spatial resolution. Relative humidity data were obtained from the ERA5 reanalysis dataset, which has a spatial resolution of 0.25°. Daily mean relative humidity values were extracted to calculate annual mean humidity.

Model data were sourced from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Detection and Attribution Model Intercomparison Project (DAMIP), a component of CMIP6, was utilized to assess the contributions of various factors to global climate change. DAMIP provides four sets of experiments: historical simulation, natural-forcing historical simulation, greenhouse-gas-forcing historical simulation, and aerosol-forcing historical simulation. The historical simulation data spans from 1961 to 2014, while the natural-forcing historical simulation, greenhouse-gas-forcing historical simulation, and aerosol-forcing historical simulation data cover the years from 1961 to 2020. We selected the historical simulation and natural-forcing historical simulation data from nine different climate models within DAMIP (Supplementary Table 3). Additionally, future climate projections based on the SSP and Representative Concentration Pathways (RCP) are provided by the CMIP6 Scenario Model Intercomparison Projection (ScenarioMIP). We chose the SSP2-4.5 (Medium Development with Medium Radiative Forcing), which is considered a continuation of the historical scenario45, for the period of 2015–2020 to complement the historical climate data.

Definition of heat stress
We identified heat stress days and extreme heat stress days using daily maximum UTCI values. Researches have consistently demonstrated that when the UTCI exceeds 26°C, the human body experiences significant physiological responses, such as an increased sweating rate and elevated core body temperature, which can lead to discomfort, illness, and even death46. Consequently, we defined heat stress days as those with a daily maximum UTCI greater than 26°C. We emphasized that although this threshold may frequently be exceeded in tropical zones, our focus lies on analyzing temporal changes in heat stress intensity and frequency over time rather than absolute values. Additionally, extreme heat stress can have more severe effects on human health47, and thus, we further identified extreme heat stress days using a relative threshold approach. While the official UTCI classification designates >46°C as ‘Extreme Heat Stress’, this absolute threshold does not account for local acclimatization or climate variability. Therefore, we employed a grid-specific and time-adaptive relative threshold, defined as the 90th percentile of daily maximum UTCI values calculated within a 15-day moving window48.

We assessed heat stress by examining both mean heat stress intensity and extreme heat stress days. (1) Mean heat stress intensity: This is characterized by the average daily maximum UTCI of heat stress days. Higher mean daily UTCI values indicate greater heat stress, suggesting that human societies are exposed to higher heat stress. (2) Extreme heat stress days: This is characterized by the number of days experiencing extreme heat stress. Since extreme heat stress can have more severe impacts on human health and society47, an increase in the number of extreme heat stress days implies a higher risk of mortality49. The number of days was chosen to represent extreme heat stress because the mean UTCI values for extreme heat stress days tend to show less variability and are not as indicative of the overall impact as the extreme heat stress days33. We employed Theil-Sen estimator, a robust non-parametric method for estimating linear trends, combined with the Mann–Kendall test, a non-parametric significance test for trend detection, to quantitatively analyze the temporal trends in both mean heat stress intensity and extreme heat stress days from 1981 to 202050.

Linking climate change to heat stress trend
To relate climate change to heat stress trend, we employed a LightGBM model. We selected LightGBM, a histogram-based Gradient Boosting Decision Tree (GBDT) implementation, because it delivers state-of-the-art accuracy, exceptional training speed (up to 20× faster than traditional GBDT)51, and efficient handling of large datasets with high feature dimensions52. The model is built upon a gradient boosting framework as Eq. (1).

F_m(x) = F_{m-1}(x) + \nu \sum_{k=1}^{K} \gamma_{mk} I(x \in R_{mk}) (1)

where F_m(x) is the ensemble prediction after adding the m-th tree. F_{m-1}(x) is the prediction from the previous boosting iteration. \nu is the learning rate. K is the number of terminal leaves in the m-th tree, \gamma_{mk} is leaf weight, and R_{mk} is the corresponding input region (leaf partition) in the feature space defined by the splits of the m-th tree. I(x \in R_{mk}) is an indicator function that equals 1 if the sample x falls into region R_{mk}, and 0 otherwise.

We used this model to estimate the trends in mean heat stress intensity and extreme heat stress days as response variables. Predictors included: trends in temperature and humidity, their climatological means, and geographical coordinates53. Model validation was performed via 10-fold cross-validation. The R² for test sets was 0.865 (mean heat stress intensity) and 0.917 (extreme heat stress days). Feature importance results indicated that temperature trend (28%) and longitude (22%) were most important for mean heat stress intensity change, while temperature trend alone dominated changes in extreme heat stress days change (35%).

Counterfactual analysis
To construct the climate series under the counterfactual condition, we applied a delta bias correction approach45,54. Specifically, we calculated temperature and relative humidity under the counterfactual condition using Eqs. (2) and (3).

T_{counterfactual}(t) = T_{ERA5}(t) - [T_{DAMIP historical}(t) - T_{DAMIP nat}(t)] (2)

RH_{counterfactual}(t) = RH_{ERA5}(t) - [RH_{DAMIP historical}(t) - RH_{DAMIP nat}(t)] (3)

Where, T_{counterfactual}(t) and RH_{counterfactual}(t) are the temperature and relative humidity series under counterfactual condition at time t; T_{ERA5}(t) and RH_{ERA5}(t) are daily values from ERA5-Land and ERA5 reanalysis datasets; T_{DAMIP historical}(t) and RH_{DAMIP historical}(t) are ensemble mean DAMIP historical simulation; and T_{DAMIP nat}(t) and RH_{DAMIP nat}(t) are ensemble mean DAMIP natural-forcing historical simulation. The temperature and relative humidity series of counterfactual condition were not used to recalculate synthetic UTCI directly, but were used as inputs to the trained LightGBM model to predict heat stress trend under counterfactual condition.

To distinguish the contributions of natural and anthropogenic climate change to heat stress trends, we defined three scenarios as follows:
ALL scenario: heat stress trends driven by the factual climate change, incorporating both natural and anthropogenic climate change, as represented by ERA5-HEAT.
NAT scenario: heat stress trends driven by natural climate change alone, predicted under the counterfactual natural-only climate series.
Anthro scenario: heat stress trends attributable solely to anthropogenic climate change, estimated by removing the NAT scenario component from the ALL scenario component.

Spatial inequality assessment
We assessed spatial inequality in heat stress trend across economies with various levels of socioeconomic development by plotting concentration curves for economy under three scenarios (ALL, NAT, and Anthro). These concentration curves illustrate how heat stress trend is distributed across economies with various socioeconomic statuses. For low-income economies, lower levels of socioeconomic development generally correspond to a reduced capacity to cope with heat stress, meaning these countries have lower adaptive capacity. As a result, increases in heat stress pose a greater risk to these countries, exacerbating their vulnerability to heat-related impacts. Therefore, increase in heat stress that disproportionately affects economies with lower socioeconomic development is considered indicative of growing spatial inequality in global heat stress.

Specifically, to measure spatial inequality in heat stress trend across economies, we ranked 157 economies in order of GDP per capita and plotted concentration curves based on the cumulative change in heat stress corresponding to the cumulative percentile of the number of economies, ordered from the poorest to the richest. Concentration index (CI) serves as a measure of spatial inequality in heat stress trend which is calculated using Eq. (4), through dividing the area between the concentration curve and the line of equality by the area above the line of equality55.

CI = 2 \int_{0}^{1} [L(p) - p] dp (4)

Where L(p) is the concentration curve and p is the cumulative proportion of economies. CI ranges from −1 to 1, with positive values indicating that the curve is above the line of equality and negative values indicating it is below. A concentration index closer to 1 signifies greater spatial inequality in heat stress trend across economies, with a special threat to low-income economics. Because the concentration curve requires non-negative variables, we applied the maximum–minimum normalization method to normalize the trends in mean heat stress intensity and extreme heat stress days to the range of [0–1]. Under this context, the diagonal line represents a uniform distribution of heat stress trend across economies, indicating absolute equality. If the concentration curve lies above the diagonal, it suggests that poorer economies have experienced more intense trends in heat stress. Conversely, if the curve is below the diagonal, it indicates that richer economies have experienced more intense trends in heat stress.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript aims to analyze the roles of natural and anthropogenic climate in driving shifts in global heat stress using human comfort indicators and to examine the disparities in heat stress across countries at varying levels of socioeconomic development.\nOverall, the study is very well written and interesting; however, areas lacking detail must be addressed. It is silent on some methodological aspects/procedures. Some sentences also lack appropriate references. Also, other aspects need to be enhanced, such as discussing the results. While promising in its approach, the manuscript suffers from some shortcomings.\nAlthough the study aims to have a global dimension, the results mainly focus on Asia, Africa, and Australia. Although some regions of North and South America and Europe present high heat stress (FIGURE 1 and 2), they are not mentioned in the results and discussion sections. In this context, it would be nice to see results presented, for instance, in Fig1 b-c, and Fig2 b-c in the regional context of each continent/region.\nThe author used the Universal Thermal Climate Index (UTCI) to analyze heat stress, but there is no discussion or comparison with other studies that also used UTCI for the same purpose. We recommend the authors to consider citing the following papers:\nhttps://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.7051\nhttps://link.springer.com/article/10.1007/s00704-023-04668-x\nrmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.7774\nThe three scenarios used (historical climate change, natural climate change, and anthropogenic climate change) must be explained.\nA major concern is about the heat stress threshold. Here, heat stress days are defined as those with a daily maximum UTCI greater than 26°C. However, most of the tropical and subtropical regions of the world have mean UTCI values that are more significant than this. For instance, https://link.springer.com/article/10.1007/s00704-023-04668-x adapted heat stress categories according to the regional climate of South America. Without this consideration, most regions are naturally over heat stress.\nSecond, the discussion lacks comparisons with and citations of previous studies, particularly on the regional scale. Please consider discussing your results based on the following papers:\nhttps://iopscience.iop.org/article/10.1088/1748-9326/ad0448/meta\nhttps://link.springer.com/article/10.1007/s00484-022-02295-1\nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295766\nhttps://www.sciencedirect.com/science/article/pii/S0277953622008322\nhttps://www.nature.com/articles/s41467-021-26050-z#Sec7\nMinor comments\nThe font size in all figures is relatively tiny.\nPage 3, Line 56: The sentence \"Most historical time series studies have primarily examined inequalities in temperature-based heat stress, rather than focusing on heat stress as directly experienced by people.\" lacks references. It is necessary to specify which studies support this claim. Please provide appropriate citations.\nPage 4, Line 90: The value \"33°C\" appears to refer to a global average for all years. If so, this value may not be meaningful. It would be more informative to present a variation of this global average by decade, for example.\nPage 4, Lines 90-111: The paragraph is too long and covers different topics. It is recommended to break it at line 98, starting from the word \"globally.\"\nPage 4, Line 99: The value of 21 days is unclear—does it refer to an annual average? Instead of a single average for the 1981-2020 period, it would be more informative to show the evolution of this value over time. Additionally, the sentence states that the year with the highest number of days had 37 days, but which year was this? This information is crucial. If this year is recent, it may also be useful to discuss how many of such years occurred in the last decade, for example.\nPage 5, Line 130: The statement that the anthropogenic impact on heat stress is much greater than that caused by natural climate change lacks specificity. How much greater is it? This is a crucial piece of information that needs to be quantified and supported with evidence.\nPage 6, Line 166: Consider breaking the paragraph at \"The Inequality Effect\" to improve readability and structure.\nGeneral: In several parts of the text, the term \"Inequality\" is used without specifying the type of inequality being discussed. It is recommended to always specify \"Regional Inequality\" for clarity.\nPage 6, Line 204: The rates of increase presented from this line need confidence intervals or uncertainties associated with them. Without these, it is difficult to make meaningful comparisons between the values.\nEnd of Chapter \"Significant Impacts of Anthropogenic Climate Change\": It would be important to contextualize other studies addressing regional inequalities related to other extremes, as presented in IPCC reports, for example. Given that events are currently occurring in combination and resulting in cascading disasters, this context helps to understand the vulnerability of these regions not only to heat stress but to climate change as a whole.\nPage 16, Methodology: Why were the Era 5 data limited to years until the year 2020? The phenomenon being studied has intensified in recent years, and 2023 was the hottest year on record. We expect this analysis to be extended to include more recent years.\nPage 357-358: A reference for the models LightGBM and GAM is missing. Please include appropriate citations for these models.\nPage 431: What is the source and year of the GDP per capita data for the countries?", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Thank you for providing the manuscript. The article presents an analysis of how heat stress trends are experienced unequally by countries at different levels of socioeconomic development. Additionally, it distinguishes the contributions of natural and anthropogenic warming, which adds insight to the discussion.\nHowever, the validity of the results heavily depends on how the contributions to heat stress are decomposed into natural and anthropogenic warming. Unfortunately, the authors do not clearly explain the methodology behind this decomposition. To ensure that readers can fully assess the results, I hope the authors can clarify the following questions.\nFirst, the manuscript mentions that 9 climate models were selected to generate historical and natural-forcing historical simulations. Could the authors specify which models were used? Additionally, how many ensemble members from each model were included in the analysis?\nSecond, the authors state that they used the “Delta method.” I assume this refers to \"quantile delta mapping\" as proposed by Cannon et al. (2015). If so, what type of distributions were applied? Did the authors construct temperature and humidity distributions at each grid cell before applying quantile delta mapping, or did they aggregate global temperature and humidity distributions before applying the method?\nAfter applying quantile delta mapping, the authors would obtain temperature and humidity distributions (as well as time series) for the synthesized natural-forcing historical period. However, how were the corresponding UTCI values for this scenario derived? Did the authors simply plug in temperature and humidity values into the UTCI calculator? If so, how were wind speed and radiative temperature handled? Or, was there a different method used to compute UTCI for the natural-forcing scenario? This distinction is important because the manuscript states that UTCI for the all-forcing scenario was directly obtain from the ERA5 dataset, but the approach for the natural-forcing scenario seems to be different.\nAfter applying the quantile delta mapping, the authors would obtain temperature/humidity distributions (and time series) for the synthesized natural-forcing historical period. But how do the authors get the synthesized UTCI values for this natural-forcing scenario? Do they simply plug in the temperature/humidity? What about the wind sped and radiating temperature? Or do the authors use different method to calculate the UTCI for the natural-forcing scenario? I asked this question because the authors used the computed UTCI from EAR5 for the all-forcing scenario, but seemed to use different method to obtain the UTCI for the natural-forcing scenario.\nWhile some of these questions may seem straightforward, a full disclosure of the methodology is essential to ensure the robustness of the results. Given the significance of the findings, I encourage the authors to provide a more detailed explanation of their approach."}, "author_response": "Dear Reviewers,\nThanks for your comments concerning our manuscript entitled “Rising heat, rising inequality: Anthropogenic climate change’s impact on global heat stress”. (No.: NCOMMS-25-02700-T). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the response to your comments are as follows:\nReviewer #1:\nReview: The authors investigated the connection between climate change and thermal comfort in economically disadvantaged areas, successfully addressing a critical challenge in distinguishing the anthropogenic and natural contributions to climate change.\nThis is an engaging and timely article that explores topics of significant importance, particularly the interplay between anthropogenic and natural factors contributing to climate change, with a focus on low- and middle-income countries. The exploration of thermal comfort as a related factor is particularly noteworthy, as it remains underexplored, especially in more economically deprived regions. Additionally, the use of open-source data is commendable and aligns with best practices for transparency and reproducibility in research.\nHowever, there are several areas requiring improvement, particularly in terms of methodological clarity and the discussion of related literature, which l will detail below.\nWhile the research demonstrates potential for publication, I do not recommend it in its current form due to these substantive concerns.\nResponse:\nWe thank Reviewer #1 for recognizing the timeliness and significance of our work. We have carefully considered your suggestions regarding methodological clarity and additional literature discussion. In the responses that follow, we detail how each concern has been addressed in the revised manuscript.\nWe trust that these enhancements address the reviewer’s concerns about technical transparency and situating our work within the broader literature. All changes are highlighted in the revised manuscript (red text), and line‑by‑line responses to each comment are provided in the accompanying response document. We appreciate the constructive feedback and hope our revisions meet with your approval.\n01. Document in general: The manuscript needs line numbering to facilitate the work of reviewers and authors.\nResponse:\nThank you for highlighting the importance of line numbering. Per the journal’s submission guidelines, our uploaded Word file is provided without line numbers because the manuscript management system automatically generates the PDF version with continuous line numbers for review. We have confirmed that the PDF available to reviewers does indeed include line numbers throughout. If you prefer to have them in the Word file as well, we would be happy to supply a version with embedded line numbers upon request.\n02. Introduction: The authors do not provide references to clarify which low- and middle-income countries are included in their analysis. I suggest incorporating appropriate references, such as the one available at https://isi-web.org/low-and-middle-income-countries-and-regions.\nResponse:\nThank you for pointing out the need to specify which countries fall into the low- and middle-income categories. We have added the following clarification to the end of the Introduction:\nAdditionally, we evaluate how these contributions vary across regions categorized by the World Bank into high-income (H), upper-middle-income (UM), lower-middle-income (LM), and low-income (L) groups 14.\nA new reference has been added:\nWorld Development Indicators. https://databank.worldbank.org/source/world-development-indicators.\nWe trust this makes our country-group definitions fully transparent.\n03. Introduction: Although the introduction briefly mentions health as a relevant issue, this aspect is not substantively explored in the main body of the article. I recommend aligning the introduction with the scope of the article or expanding the discussion of health impacts to ensure consistency.\nResponse:\nWe sincerely thank the reviewer for this important suggestion. To address this, we have significantly expanded the discussion of health impacts by integrating recent findings on the health and productivity impacts of heat stress:\nStudies have shown that specific working populations, including migrants, pregnant women, and outdoor laborers, are more vulnerable to heat stress due to combined physiological and occupational exposure 20,21. In India’s coastal cities, for example, rising SHSI values suggest a substantial decline in work performance in future decades, especially under higher SSP scenarios 22. At the global level, more than 70% of the workforce is currently exposed to severe heat risk, underscoring the critical need for global labor protection strategies under rising temperatures 23.\n04. Introduction: The state-of-the-art section could be more thoroughly developed to provide a robust foundation for the study. l will offer suggestions for additional references in the discussion section, which may also be relevant for enriching the introduction.\nResponse:\nWe sincerely thank the reviewer for this insightful suggestion. In response, we have carefully revised the Introduction section. Specifically, we have expanded our discussion of the limitations of conventional temperature-based assessments and emphasized recent progress in utilizing comprehensive human-perceived thermal comfort indicators (such as the UTCI, wet-bulb temperature, and net effective temperature). Additionally, we more clearly articulate how this study addresses existing research gaps concerning the differential impacts of anthropogenic versus natural climate factors on heat stress.\nSpecifically, we now report that:\nCurrent research increasingly emphasizes the importance of moving beyond conventional temperature-based analyses to evaluate human-perceived heat stress more comprehensively 6,7. Metrics such as the Universal Thermal Climate Index (UTCI), wet-bulb temperature, and net effective temperature have emerged as critical tools for capturing complex interactions between temperature, humidity, radiation, and wind, providing a more accurate assessment of thermal comfort and human health 8,9. Despite these advancements, key research gaps remain. Most notably, existing studies have predominantly investigated heat stress using simple temperature-based indices 10,11, with limited exploration of historical variability and regional inequality using human-perceived thermal stress indicators.\nAddressing these research gaps requires a systematic examination across three core areas: (1) detailed characterization of the spatial and temporal dynamics of global heat stress using indicators reflective of human thermal comfort; (2) rigorous attribution of heat stress changes to anthropogenic and natural climatic factors; and (3) an explicit analysis of regional inequalities in heat stress impacts among countries differing in socioeconomic development.\nSpecifically, this study aims to precisely identify the distinct roles played by anthropogenic and natural climate change in driving global heat stress increases and to quantify how these climatic forces differently impact countries across socioeconomic development level, thus addressing critical gaps in climate equity research.\nMoreover, we appreciate your note that additional references suggested later in your comments may also be relevant to enriching the introduction. Accordingly, we have carefully reviewed all subsequent suggestions and have incorporated relevant literature into both the revised Discussion. These updates help position our work more clearly within the broader research landscape and reinforce the novelty and necessity of our approach.\nThank you again for your constructive and detailed guidance, which has substantially improved the framing of our manuscript.\n05. Result: A significant number of trend values are presented in this section. To enhance clarity and accessibility for the reader, I recommend summarizing these values in a table format.\nResponse:\nThank you for this valuable suggestion. To improve clarity and allow readers to quickly locate key trend values, we have added two summary tables in the revised manuscript:\nTable 1. Trends in mean heat stress intensity and extreme heat stress days.\nGlobal heat stress trends 1981 to 2020 1981 to 2000 2001 to 2020\nMean heat stress intensity 0.012°C·yr-1 0.008°C·yr-1 0.017°C·yr-1\nExtreme heat stress days 0.429 days·yr-1 0.260 days·yr-1 0.772 days·yr-1\nThis table succinctly present the global and key regional trends for both mean heat stress intensity and extreme heat stress days, alongside the percentages of areas experiencing significant increases. We believe this tabular format greatly enhances accessibility and comparability of the results.\n06. Result: The application of the Köppen-Geiger classification could be more thoroughly explored, particularly in the context of the inequality and trends sections. This would strengthen the connection between the classification and the study's findings.\nResponse:\nWe thank the reviewer for emphasizing the value of climate zones. After careful consideration, we determined that Köppen–Geiger was not used as a grouping variable in final trend or inequality analyses. Its initial purpose was to inform exploratory GAM fitting (now removed). To avoid misconceptions, we have removed all references to Köppen–Geiger in the context of results.\n07. Result: The inequality section would benefit from the inclusion of a map that highlights the regions with the highest inequalities. Such a visual aid would provide readers with a clearer understanding of the spatial distribution of inequalities.\nResponse:\nThank you for the suggestion to include a spatial map of inequality “hotspots.” Because our inequality metric is computed via a concentration index across countries ranked by GDP per capita, it yields one scalar value per scenario, rather than a spatially varying field. In other words, for each scenario, we calculate a single concentration index that summarizes how changes in heat stress are distributed across all 157 countries, not across grid cells.\nTo clarify this approach, we have expanded Section “Regional inequality assessment” as follows:\nSpecifically, to measure the regional inequality in heat stress, we ranked 157 countries in order of GDP per capita and plot concentration curves based on the cumulative change in heat stress corresponding to the cumulative percentile of the number of countries, moving from the poorest to the richest. The concentration index (CI) serves as a measure of regional inequality and is calculated by dividing the area between the concentration curve and the line of equality (S) by the area above the line of equality 52.\nBecause the resulting CI is not spatially resolved (it does not produce a grid of values), a choropleth map of “inequality intensity” is not directly applicable. Instead, we present concentration curves (Fig. 5f and 5g) to visualize how heat stress changes accumulate among poorer vs. richer countries. We hope this clarification makes our national-scale inequality framework and its visualization rationale entirely clear.\n08. Result: Maps 1 to 5 are missing a scale. While they include essential elements such as a legend, north arrow, and coordinates, adding a scale is necessary for accurate interpretation.\nResponse:\nThank you. We have now added scale bars to Maps 1–5 (Figures 1–5), each placed in the lower left/right corner to conform with cartographic standards. All maps retain their legends and coordinate grids for complete geospatial context.\n09. Result: The results are presented on a global scale, but the focus of the study is on low-and middle-income countries. Linked to this Australia and other parts of the globe which are not low-and middle-income countries are mentioned and analyzed. To better align with the study's objectives, the results should be scaled to emphasize the geographic regions of interest.\nResponse:\nThank you for pointing out the need to foreground low- and middle-income regions. We have revised Section “Global mean heat stress intensity and extreme heat stress days” to de-emphasize Australia and instead provide a more balanced global overview, with particular attention to regions within low- and middle-income country groupings. Specifically:\n Mean Heat Stress Intensity: We now write:\nComparison of trends in mean heat stress intensity over time across regions (Fig. 1d). At the regional scale, a total of 37 regions had higher trend means for 2001-2020 compared to 1981-2000. Eastern and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trends of 0.081°C·yr-1 and 0.071°C·yr-1, respectively, across the two time periods.\nWhile eastern and northern Australia exhibit strong upward trends, equally dramatic increases are observed in parts of Africa (western and eastern South Africa) and South America (northwestern and northeastern regions).\n Extreme Heat Stress Days: The text now reads:\nComparison of trends in extreme heat stress days across regions over time (Fig. 2d). At the regional scale, a total of 40 regions had higher trend means for 2001-2020 compared to 1981-2000. Northern and central Australia experienced the most dramatic increases in extreme heat stress days, with trends of 2.247 days·yr-1 and 2.030 days·yr-1 over the two periods, respectively. Beyond Australia, the greatest growth in extreme heat stress days occurs across the low latitude tropics, including Central Africa, northern and southern East Africa, eastern South Africa, and Madagascar.\nMeanwhile, our detailed comparisons among low-, lower-middle, upper-middle, and high-income country groups remain in Section “Regional inequality of global heat stress”, ensuring that the manuscript’s core focus—inequalities across economic strata—is clearly retained.\n10. Discussion: While the discussion is engaging and provides a detailed explanation of the results, it currently lacks a robust comparison with findings from previous research. To strengthen the discussion, the authors should incorporate a more thorough exploration of the existing literature and contextualize their findings within the broader body of research.\nResponse:\nWe sincerely thank the reviewer for this constructive observation. We agree that a more thorough engagement with the existing literature is essential for positioning our findings within the broader scientific context.\nIn response, we have revised the Discussion section to incorporate more explicit comparisons with relevant studies, especially those using UTCI and other human-centric heat stress metrics, as well as those addressing regional disparities and attribution of climate-driven extremes. These revisions draw on the references kindly suggested in later comments, and also integrate additional foundational and recent work to support and contrast our findings.\nSpecifically, we now report that:\nOver the past four decades, a pervasive rise in heat stress across every inhabited continent, with the annual number of hours above UTCI 32 °C climbing significantly in Europe (1979-2019) 15, the Caribbean and subtropical Atlantic 16, South America 17and even high-latitude Beringia 18. Our global result—that 52% and 67% of all global regions showed a significant increase in mean heat stress intensity and extreme heat stress days—therefore consolidates a broad empirical consensus.\nA recent probabilistic projection further emphasizes that even under 2°C warming scenarios, exposure to dangerous heat stress levels may increase dramatically in the tropics and midlatitudes, with a tenfold increase likely in many regions by 2100 19.\nWe believe that these additions significantly strengthen the scholarly grounding and relevance of the discussion, and we thank you again for your insightful input.\n11. Discussion: The state of the literature presented is limited and could be expanded to provide a deeper and more nuanced understanding of the topic. I recommend incorporating the following references, which could add valuable perspectives and insights:\nhttps://doi.org/10.1038/s43247-022-00524-4\nhttps://doi.org/10.1016/jjoclim.2023.100249\nhttps://doiorg/10.1186/s12889-024-19212-3\nhttps://doi.org/10.1007/s11069-024-06872-y\nhttps://www.weforum.org/stories/2024/08/extreme-heat-workers-climate-health/\nhttps://doi.org/10.1007/s00484-024-02639-z\nBy leveraging these references, the authors can better situate their findings within the context of existing knowledge and provide a more comprehensive discussion.\nResponse:\nWe sincerely appreciate this valuable suggestion and have incorporated recommended references into the revised manuscript.\nSpecifically, we now report that:\nA recent probabilistic projection further emphasizes that even under 2°C warming scenarios, exposure to dangerous heat stress levels may increase dramatically in the tropics and midlatitudes, with a tenfold increase likely in many regions by 2100 19.\nStudies have shown that specific working populations, including migrants, pregnant women, and outdoor laborers, are more vulnerable to heat stress due to combined physiological and occupational exposure 20,21. In India’s coastal cities, for example, rising SHSI values suggest a substantial decline in work performance in future decades, especially under higher SSP scenarios 22. At the global level, more than 70% of the workforce is currently exposed to severe heat risk, underscoring the critical need for global labor protection strategies under rising temperatures 23.\nEach citation is not only included but discussed meaningfully in relation to our results, as per the reviewer’s request.\n12. Methodology: One overall methodological scheme is needed.\nResponse:\nWe appreciate this comment and have added a concise but comprehensive overview at the beginning of the revised methodology section. The first paragraph now introduces a stepwise workflow that includes UTCI derivation, trend estimation, statistical modeling, counterfactual analysis, and inequality assessment.\nSpecifically, we now report that:\nTo systematically quantify and attribute global heat stress changes, we implemented a five‑step workflow. First, we extracted daily maximum Universal Thermal Climate Index (UTCI) values from the ERA5‑HEAT reanalysis (1981-2020) to compute mean heat stress intensity and annual extreme‑heat‑stress day counts. Second, we quantified temporal trends using the robust Theil–Sen slope estimator paired with the Mann–Kendall non‑parametric test to assess statistical significance. Third, we trained a Light Gradient Boosting Machine (LightGBM) model—chosen for its speed, scalability, and high accuracy in nonlinear regression—to link local trends in temperature, humidity, and geographic coordinates to observed heat stress changes. Fourth, we disentangled anthropogenic from natural influences by constructing a NAT‑only scenario via delta bias correction of ERA5 observations using CMIP6 DAMIP ALL‐ and NAT‑forcing ensembles, then projecting heat stress under NAT-only conditions through our LightGBM model; the anthropogenic contribution was defined as the difference between factual and NAT-only projections. Finally, we assessed the regional inequality of global heat stress by ranking countries according to 2020 GDP per capita and computing concentration indices under historical, NAT-only, and Anthro-only scenarios.\n13. Methodology: A table identifying which DAMIP models were used is needed.\nResponse:\nThank you for this useful suggestion. We have included Table 1 in the revised “Data preprocessing” subsection, listing all nine CMIP6 DAMIP models, their ensemble member (r1i1p1f1), time periods, and spatial resolutions. This clarifies the composition of the model ensemble used for attribution analysis.\nSpecifically, we now report that:\nTable 2. Climate models within DAMIP used.\nEnsemble\nHistorical Natural-Forced\nModel Members Resolution\nSimulation Simulation\nUsed\nACCESS-ESM1-5 r1i1p1f1 1961–2014 1961–2020 1.875°×1.25°\nBCC-CSM2-MR r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nCanESM5 r1i1p1f1 1961–2014 1961–2020 2.8°×2.8°\nFGOALS-g3 r1i1p1f1 1961–2014 1961–2020 2.25°×2°\nGFDL-ESM4 r1i1p1f1 1961–2014 1961–2020 1.25°×1°\nIPSL-CM6A-LR r1i1p1f1 1961–2014 1961–2020 2.5°×1.25°\nMIROC6 r1i1p1f1 1961–2014 1961–2020 1.4°×1.4°\nMRI-ESM2-0 r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nNorESM2-LM r1i1p1f1 1961–2014 1961–2020 2.5°×1.875°\n14. Methodology: A table identifying which CMlP6 models were used is needed.\nResponse:\nWe clarify that all models were sourced from CMIP6 via the DAMIP experiment and that we used the ALL and NAT scenarios only. The full model list and associated details are now explicitly presented in Table 2.\nSpecifically, we now report that:\nTable 2. Climate models within DAMIP used.\nEnsemble\nHistorical Natural-Forced\nModel Members Resolution\nSimulation Simulation\nUsed\nACCESS-ESM1-5 r1i1p1f1 1961–2014 1961–2020 1.875°×1.25°\nBCC-CSM2-MR r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nCanESM5 r1i1p1f1 1961–2014 1961–2020 2.8°×2.8°\nFGOALS-g3 r1i1p1f1 1961–2014 1961–2020 2.25°×2°\nGFDL-ESM4 r1i1p1f1 1961–2014 1961–2020 1.25°×1°\nIPSL-CM6A-LR r1i1p1f1 1961–2014 1961–2020 2.5°×1.25°\nMIROC6 r1i1p1f1 1961–2014 1961–2020 1.4°×1.4°\nMRI-ESM2-0 r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nNorESM2-LM r1i1p1f1 1961–2014 1961–2020 2.5°×1.875°\n15. Methodology: The use of ERA5 data to assess UTCI provides a valuable global perspective on heat stress conditions across extensive geographical areas (0.25°resolution, approximately 110 km). However, the authors should include a reflection on the implications of this resolution for analyzing localized heat stress and its potential limitations in capturing finer-scale variability, particularly in low- and middle-income regions.\nResponse:\nThank you for this useful suggestion. We have added a new paragraph to Section “Discussion”, acknowledging that:\nSecond, While the 0.25° (~25 km) resolution of ERA5 HEAT provides robust global coverage, it cannot resolve micro scale phenomena such as urban heat islands, fine‐scale land cover heterogeneity, or localized shade effects. As a result, our findings primarily reflect broad regional trends, and caution is warranted when interpreting results for individual cities or sub urban locales in low- and middle-income regions. We recommend future work employ high‐resolution downscaled UTCI products or station networks to capture intra urban variability.\nThis discussion both acknowledges limitations and suggests concrete paths for finer‐scale follow‐up studies.\n16. Methodology: The terminology related to \"risk\" is not applied accurately in the manuscript. The authors appear to be assessing vulnerability by examining population inequalities in heat stress, as well as evaluating population exposure and susceptibility to heat stress. It is essential to reframe these concepts and align their use with established definitions in the field, such as those provided by the IPCC or related literature, to ensure conceptual clarity and consistency.\nResponse:\nWe sincerely thank the reviewer for this constructive observation. We have revised our manuscript to align with the definitions provided by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report (AR6). Specifically, we have removed any instances where \"heat stress\" was equated directly with \"risk.\" Instead, our focus is on assessing \"exposure\" and \"vulnerability\" to heat stress.\n17. Methodology: The authors imply a direct causal link between heat stress and mortality. While the connection is evident, it is important to clarify that individuals typically do not die solely from heat stress. Mortality is often a result of pre-existing health conditions exacerbated by heat stress. The authors should refine their statements to reflect this complexity. Human adaptation to extreme heat, both biological and behavioral, should also be considered when discussing the impacts of heat stress. For instance, populations in regions like Niger endure prolonged periods of extreme heat stress yet continue to live there. This highlights the importance of adaptation, which influences well-being and may indirectly impact mortality rates.\nTo substantiate these points within the scope of this study, I recommend that the authors consider analyzing 2-3 specific cities as case studies. By linking available mortality data with the UTCI data used in this research, they could provide a more robust and localized perspective on the relationship between heat stress and mortality. Or, l would recommend reframing the concept of mortality to something around well-being and worsening health. The term morbidity could also be used instead.\nResponse:\nWe thank the reviewer for this thoughtful and important comment. We fully agree that heat-related mortality is typically mediated by underlying health conditions and that direct attribution to heat stress alone oversimplifies a complex causal pathway.\nIn response, we have carefully revised all instances in the manuscript that may have implied a deterministic or singular causal link between heat stress and mortality.\nWe further recognize the importance of human adaptation—both biological (e.g., acclimatization) and behavioral (e.g., work-rest cycles, clothing, infrastructure design)—in mediating the health impacts of heat. This has been briefly discussed in the revised Discussion section, with reference to recent studies that highlight the heterogeneity of population-level heat resilience.\nSpecifically, we now report that:\nOur analysis focused solely on the geospatial scale of the regional inequality in heat stress, but different populations within the same geographic area may have varied adaptations to heat stress due to physiological and socioeconomic differences, resulting in differing vulnerabilities when facing heat stress 33. Future research should consider differentiating among various vulnerable groups within countries or regions, such as different ethnicities, age groups, occupational groups, educational groups, and more 2,34. This would allow for a more nuanced assessment of regional inequality in heat stress by considering the specific vulnerabilities and adaptation capacities of different population groups.\nRegarding the suggestion to incorporate 2–3 city-level case studies linking UTCI to observed mortality: while we agree that such analysis would offer valuable localized insights, our current study is global in scope and based on gridded UTCI data at 0.25° (~25 km) resolution. At this scale, it is difficult to robustly match UTCI trends with observed mortality rates, which are typically reported at finer administrative or demographic levels and influenced by health system variables beyond the reach of our dataset. We have acknowledged this limitation explicitly in the “Discussion” section and suggested it as a promising direction for future interdisciplinary research.\nSpecifically, we now report that:\nSecond, While the 0.25° (~25 km) resolution of ERA5 HEAT provides robust global coverage, it cannot resolve micro scale phenomena such as urban heat islands, fine‐scale land cover heterogeneity, or localized shade effects. As a result, our findings primarily reflect broad regional trends, and caution is warranted when interpreting results for individual cities or sub urban locales in low- and middle-income regions. We recommend future work employ high‐resolution downscaled UTCI products or station networks to capture intra urban variability.\nWe sincerely appreciate the reviewer’s recommendations, which have helped us improve the conceptual clarity and public health relevance of our study.\n18. Methodology: The authors developed their own methodology to define extreme heat stress. However, the UTCI framework already provides a standard classification, with extreme heat stress defined as UTCI temperatures exceeding 46°C. It is unclear why the authors opted not to use this classification. ls their classification significantly different or more applicable to the study's focus? Clarification on this point is needed.\nResponse:\nWe appreciate the reviewer’s point. In Section “Definition of heat stress” we now clarify:\nExtreme heat stress days were identified using a relative threshold approach: The official UTCI classification designates > 46 °C as ‘Extreme Heat Stress’; however, because absolute thresholds do not account for local acclimatization and climate diversity, we employ a relative threshold (90th percentile of daily maxima within a 15-day moving window) 46.\nWe cite some study to justify our focus on relative human-comfort extremes rather than a single global cutoff.\n19. Methodology: The choice of LightGBM and GAM models requires further justification. Are there bibliographical references supporting their use in similar studies? If not, the authors should provide a detailed explanation of why these models were chosen, highlighting their advantages over alternative statistical methods. Additionally, the methodology section would benefit from including the relevant formulas or equations for both LightGBM and GAM to enhance transparency and reproducibility.\nResponse:\nTo address this, we have expanded Section “Climate-heat stress modeling”:\nGAM Removed: In the revised manuscript, we have removed GAM entirely, as it did not yield additional insights beyond LightGBM and was not used in the final analysis.\nLightGBM Justification: We selected LightGBM—a histogram-based Gradient Boosting Decision Tree implementation—because it delivers state-of-the-art accuracy, exceptional training speed (up to 20× faster than traditional GBDT), and efficient handling of large datasets with high feature dimensions.\nSpecifically, we now report that:\nTo relate climate trends to changes in heat stress, we employed a Light Gradient Boosting Machine (LightGBM) model. We selected LightGBM—a histogram‑based Gradient Boosting Decision Tree implementation—because it delivers state‑of‑the‑art accuracy, exceptional training speed (up to 20× faster than traditional GBDT) 32, and efficient handling of large datasets with high feature dimensions49. Key Formula (Boosting Framework):\n𝐾\n𝐹 (𝑥)=𝐹 (𝑥)+𝜈∑ 𝛾 {𝑥 ∈𝑅 }\n𝑚 𝑚−1 𝑚𝑘 𝑚𝑘\n𝑘=1\nwhere 𝐹 is the ensemble prediction after 𝑚 trees, 𝜈 is the learning rate, 𝛾 are leaf\n𝑚 𝑚𝑘\nweights, and 𝑅 are the partition regions determined by the 𝑚-th tree. This formulation underlies\n𝑚𝑘\nLightGBM’s split-wise (leaf-wise) tree growth strategy.\nThese additions ensure methodological transparency and reproducibility.\n20. Methodology: Regarding the analysis of inequality, the inclusion of tree cover percentage as a variable could add depth to the identification of vulnerable areas. Given the well-documented role of greenery and shade in mitigating heat stress and serving as climate refuges, tree cover is a valuable factor that should be considered.\nResponse:\nWe completely agree that vegetation buffers are important. However, Global tree cover datasets (e.g., MODIS VCF) are only available from 2000 onward, limiting compatibility with our 1981–2020 analysis.\nOur study focuses on assessing spatial disparities in heat stress trends, not identifying biophysical drivers. We hope this explanation clarifies why tree cover was not included, while acknowledging its importance for follow-on research.\n21. Methodology: The authors used air temperature and relative humidity as explanatory variables but did not explicitly justify their choice. Other variables, such as radiation or windspeed, may have a greater influence on heat stress. To address this, the authors could analyze these four variables (air temperature, relative humidity, radiation, and wind speed) using Principal Component Analysis (PCA). This approach would allow for the selection of the most influential variables to serve as explanatory factors. As it stands, the methodological rationale is insufficient and warrants further elaboration.\nResponse:\nWe clarified this in the Section “Climate-heat stress modeling”. Moreover, we used LightGBM's built-in feature importance evaluation, which showed temperature trend and mean temperature as the most important variables (35% and 22% importance, respectively).\nSpecifically, we now report that:\nTo relate climate trends to changes in heat stress, we employed a Light Gradient Boosting Machine (LightGBM) model. We selected LightGBM—a histogram‑based Gradient Boosting Decision Tree implementation—because it delivers state‑of‑the‑art accuracy, exceptional training speed (up to 20× faster than traditional GBDT) 32, and efficient handling of large datasets with high feature dimensions49.\nWe used it to model the trends in mean heat stress intensity and extreme heat stress days as response variables. Predictors included: trends in temperature and humidity, their climatological means, and geographic coordinates 50. Model validation was performed via 10-fold cross-validation.\nThe R² for test sets was 0.865 (mean intensity) and 0.917 (extreme days). Feature importance results indicated that temperature trend (28%) and longitude (22%) were most important for mean intensity, while temperature trend alone dominated for extreme heat days (35%)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors investigated the link between climate change and thermal comfort in economically disadvantaged regions, effectively addressing the complex challenge of distinguishing between anthropogenic and natural contributions to climate change.\nI found this paper a pleasant and engaging read. It makes a valuable contribution to an important and timely topic that warrants further attention — the anthropogenic influence on heat stress conditions.\nThe authors made a commendable effort to address all the points I raised in the first round of revisions. These changes have significantly improved the manuscript. I believe it is now close to being ready for publication, pending a few minor revisions and suggestions that could further enhance the quality of the work.\nDocument in general\n1. Abbreviations: The manuscript contains several instances of duplicated or delayed abbreviation definitions.\nFor example:\nUTCI is correctly defined in the abstract but is unnecessarily redefined in lines 49, 74, and 280.\nLightGBM is first introduced in line 77, yet it is redefined again in lines 127, 284, and 353.\nCMIP and DAMIP are mentioned in line 288 but are not defined until lines 310 and 311.\nNAT is only explained in line 373, despite being introduced earlier in the manuscript.\nI recommend a careful review to ensure that abbreviations are defined only once—at their first mention—and used consistently thereafter. This will improve clarity and eliminate redundancy.\n2. Figures: The size of figures 3 and 4 should be increased. As it is they are difficult to read.\n3. In the results, I believe it would be interesting to further explore the trends of Mean heat stress intensity and Extreme heat stress days results, per continent (or other sub-geographical divisions), if possible. This would significantly improve the results section. So, table 1 would be as it is + geographical trends.\n4. In the discussion (line 200) the reader is introduced to “perceived” concept which is different from what the authors are investigating. Thermal comfort perception entails subjective conditions to understand thermal comfort other than just heat budget models such as the UTCI which considers meteorological, metabolic, physiological and clothing conditions.\nFor further understanding, I suggest some papers in which the definitions within the study of thermal comfort are clearly given.\nFor the perception and other subjective fields you can find the definitions here: https://doi.org/10.1007/s44327-024-00020-8\nFor what the authors are doing, which is researching about thermophysiological comfort which is how the body reacts to certain environmental conditions, I suggest this paper: https://doi.org/10.1016/j.uclim.2024.101933\nI either suggested the authors to remove the perception component of thermal comfort and leave it as human thermal comfort or just change to human-thermophysiological comfort.\n5. In the discussion, I would like the authors to delve into the reasons why maybe low or low-medium income countries have higher influence of anthropogenic induced heat than other regions. Is it the lack of investment in environmental policies and environmental education? This could make a useful reflection since the authors mentioned in the introduction that this research could be useful for policy-making information.", "reviewer_2": "Thank you for the revisions — we appreciate the improved effort to present results in a broader regional context. However, we still find that the quantitative focus remains disproportionately centered on Australia. Although Figures 1 and 2 clearly show that regions in South America (especially Brazil) and parts of Africa are among the most affected, these areas receive much less detailed attention in the text.\nWhile Australia is described with specific quantitative trends, other highly impacted regions are only referenced in general qualitative terms. We suggest including similarly detailed quantitative information for these other regions — particularly South America and Africa — to better reflect the global nature of the study.\nMoreover, Figure 3 indicates that the anthropogenic contribution to heat stress is substantially higher in Brazil and many parts of Africa than in Australia. This important finding deserves more emphasis and discussion in the text.\nIn the sentence 'we employ a relative threshold (90th percentile of daily maxima within a 15-day moving window)', please specify whether a single global threshold was used, or whether the threshold was calculated per region or per grid point.\nWe recommend avoiding the term 'racial minorities' in the Brazilian context, as Black and Brown people constitute the majority (~55% according to the Brazilian Institute of Geography and Statistics) of the population (unlike in the United States, where the term is more appropriate, for example). Instead, we suggest using terms like 'underrepresented racialized groups' or 'historically marginalized racial groups', which more accurately reflect Brazil's social and structural realities.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Thank you for the thorough revisions. I think the manuscript is much improved."}, "author_response": "Dear Reviewers,\nThanks for your comments concerning our manuscript entitled “Rising heat, rising inequality: Anthropogenic climate change’s impact on global heat stress”. (No.: NCOMMS-25-02700-T). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the response to your comments are as follows:\nReviewer #2:\nThe manuscript aims to analyze the roles of natural and anthropogenic climate in driving shifts in global heat stress using human comfort indicators and to examine the disparities in heat stress across countries at varying levels of socioeconomic development.\nOverall, the study is very well written and interesting; however, areas lacking detail must be addressed. It is silent on some methodological aspects/procedures. Some sentences also lack appropriate references. Also, other aspects need to be enhanced, such as discussing the results. While promising in its approach, the manuscript suffers from some shortcomings.\nResponse:\nWe thank Reviewer #2 for the positive evaluation and for these insightful comments. Below we address each point in detail.\n01. Although the study aims to have a global dimension, the results mainly focus on Asia, Africa, and Australia. Although some regions of North and South America and Europe present high heat stress (FIGURE 1 and 2), they are not mentioned in the results and discussion sections. In this context, it would be nice to see results presented, for instance, in Fig1 b-c, and Fig2 b-c in the regional context of each continent/region.\nResponse:\nThank you for drawing attention to this imbalance. In the revised manuscript, we have supplemented the results presented in the regional context for each continent/region in Fig 1 and 2. We have revised Section “Global mean heat stress intensity and extreme heat stress days” to de-emphasize Australia and instead provide a more balanced global overview, with particular attention to regions within low- and middle-income country groupings. Specifically:\n Mean Heat Stress Intensity: We now write:\nComparison of trends in mean heat stress intensity over time across regions (Fig. 1d). At the regional scale, a total of 37 regions had higher trend means for 2001-2020 compared to 1981-2000. Eastern and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trends of 0.081°C·yr-1 and 0.071°C·yr-1, respectively, across the two time periods.\nWhile eastern and northern Australia exhibit strong upward trends, equally dramatic increases are observed in parts of Africa (western and eastern South Africa) and South America (northwestern and northeastern regions).\n Extreme Heat Stress Days: The text now reads:\nComparison of trends in extreme heat stress days across regions over time (Fig. 2d). At the regional scale, a total of 40 regions had higher trend means for 2001-2020 compared to 1981-2000. Northern and central Australia experienced the most dramatic increases in extreme heat stress days, with trends of 2.247 days·yr-1 and 2.030 days·yr-1 over the two periods, respectively. Beyond Australia, the greatest growth in extreme heat stress days occurs across the low latitude tropics, including Central Africa, northern and southern East Africa, eastern South Africa, and Madagascar.\n02. The author used the Universal Thermal Climate Index (UTCI) to analyze heat stress, but there is no discussion or comparison with other studies that also used UTCI for the same purpose. We recommend the authors to consider citing the following papers:\nhttps://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.7051\nhttps://link.springer.com/article/10.1007/s00704-023-04668-x\nhttps://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.7774\nResponse:\nWe thank the reviewer for this highly relevant suggestion. We have carefully reviewed and now incorporated the recommended UTCI studies.\nSpecifically, we now report that:\nOver the past four decades, a pervasive rise in heat stress across every inhabited continent, with the annual number of hours above UTCI 32 °C climbing significantly in Europe (1979-2019) 15, the Caribbean and subtropical Atlantic 16, South America 17and even high-latitude Beringia 18. Our global result—that 52% and 67% of all global regions showed a significant increase in mean heat stress intensity and extreme heat stress days—therefore consolidates a broad empirical consensus.\nThese studies help reinforce the robustness of our methodological choices and provide important context for our observed spatial patterns.\n03. The three scenarios used (historical climate change, natural climate change, and anthropogenic climate change) must be explained.\nResponse:\nThank you for this valuable suggestion. We agree that greater clarity is required. In Section “Counterfactual analysis” we have added:\nTo distinguish the respective contributions of natural and anthropogenic climate change to heat stress trends, we employed a counterfactual analysis framework based on bias-corrected model data. Specifically, three climate scenarios as follows 43,51:\nHistorical (factual) scenario (ALL): Includes both natural and anthropogenic forcing, derived from reanalysis (ERA5) and historical CMIP6 ALL-forcing simulations.\nNatural-only scenario (NAT): Includes only natural external forcing (e.g., solar and volcanic), derived from CMIP6 DAMIP NAT-forcing experiments.\nAnthropogenic-only scenario (Anthro): Estimated by removing the NAT component from the ALL component, representing the climate influence attributable solely to human activities.\nWe hope this provides full transparency on our scenario construction.\n04. A major concern is about the heat stress threshold. Here, heat stress days are defined as those with a daily maximum UTCI greater than 26°C. However, most of the tropical and subtropical regions of the world have mean UTCI values that are more significant than this. For instance, https://link.springer.com/article/10.1007/s00704-023-04668-x adapted heat stress categories according to the regional climate of South America. Without this consideration, most regions are naturally over heat stress.\nResponse:\nWe have revised the Section “Definition of heat stress” to clearly state that our focus is not on baseline heat stress levels, but on changes over time (i.e., the trend). This reduces the concern about tropical bias. Moreover, we supplement this with the relative threshold method for identifying extreme heat stress days.\nSpecifically, we now report that:\nWe emphasize that although this threshold may frequently be exceeded in tropical zones, our focus lies on analyzing temporal changes in heat stress intensity and frequency over time rather than absolute values. Additionally, extreme heat stress can have more severe effects on human health 45, so we further identified extreme heat stress days. Extreme heat stress days were identified using a relative threshold approach: The official UTCI classification designates > 46 °C as ‘Extreme Heat Stress’; however, because absolute thresholds do not account for local acclimatization and climate diversity, we employ a relative threshold (90th percentile of daily maxima within a 15-day moving window) 46.\n05. Second, the discussion lacks comparisons with and citations of previous studies, particularly on the regional scale. Please consider discussing your results based on the following papers:\nhttps://iopscience.iop.org/article/10.1088/1748-9326/ad0448/meta\nhttps://link.springer.com/article/10.1007/s00484-022-02295-1\nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295766\nhttps://www.sciencedirect.com/science/article/pii/S0277953622008322\nhttps://www.nature.com/articles/s41467-021-26050-z#Sec7\nResponse:\nThank you for highlighting the need to reinforce the regional context. We have integrated all five listed regional studies.\nSpecifically, we now report that:\nWe found significant inequalities in the impact of climate change on global heat stress, particularly affecting low- and lower-middle-income countries, which experience substantially higher increases in heat stress compared to high- and upper-middle-income countries. This disparity, primarily driven by anthropogenic climate change, poses a severe threat to the socio-economic development and livelihoods of people in these countries. This observation is consistent with recent regional studies demonstrating heat-related inequality in mortality and productivity across the Global South. For example, in South Africa, inequality increases sharply at higher temperatures, especially affecting agricultural workers and poorer households with lower adaptive capacity 24. In African equatorial regions, cooling demand is projected to become infeasible with traditional infrastructure, placing significant stress on energy and health systems 25. In Brazil, studies show that heat-related excess mortality is disproportionately concentrated among older adults, racial minorities, and low-education populations 26, and in Latin American cities, socioeconomic characteristics such as poverty and segregation also modify the impact of heat-related mortality 27. In Europe, regional economic damages from extreme heat already exceed 1% of GDP in vulnerable areas and are projected to rise significantly without additional mitigation and adaptation 28.\n06. Minor comments: The font size in all figures is relatively tiny.\nResponse:\nWe appreciate this important readability concern. In the revised manuscript we have:\n Increased the font of the text elements.\n Adjusted panel layouts to maintain clarity with larger fonts, ensuring no overlap of data and text.\n07. Minor comments: Page 3, Line 56: The sentence \"Most historical time series studies have primarily examined inequalities in temperature-based heat stress, rather than focusing on heat stress as directly experienced by people.\" lacks references. It is necessary to specify which studies support this claim. Please provide appropriate citations.\nResponse:\nThank you for highlighting this. We have inserted two foundational citations immediately following this sentence:\nMost notably, existing studies have predominantly investigated heat stress using simple temperature-based indices 10,11, with limited exploration of historical variability and regional inequality using human-perceived thermal stress indicators.\nFull references now appear in the bibliography:\n Gasparrini, A. et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet 386, 369–375 (2015).\n Tuholske, C. et al. Global urban population exposure to extreme heat. Proceedings of the National Academy of Sciences 118, e2024792118 (2021).\n08. Minor comments: Page 4, Line 90: The value \"33°C\" appears to refer to a global average for all years. If so, this value may not be meaningful. It would be more informative to present a variation of this global average by decade, for example.\nResponse:\nWe thank the reviewer for this valuable observation. We agree that presenting a single global average (33 °C) for the entire study period does not offer substantial interpretive value and may be misleading given the high spatial and temporal variability in UTCI. In response, we have removed this sentence from the manuscript and have instead focused on analyzing the temporal trends and regional spatial patterns in mean heat stress intensity, which better reflect the dynamics and heterogeneity of heat stress over time.\nLikewise, to maintain consistency and clarity, we also removed the sentence reporting the mean and maximum number of extreme heat stress days (i.e., “Globally, the number of extreme heat stress days was about 21 days between 1981 and 2020, with a maximum of 37 days in the highest recorded year.”). Instead, we now emphasize the temporal evolution of extreme heat stress days using trend-based metrics, which aligns more directly with the core objectives of our analysis.\n09. Minor comments: Page 4, Lines 90-111: The paragraph is too long and covers different topics. It is recommended to break it at line 98, starting from the word \"globally.\"\nResponse:\nWe sincerely thank the reviewer for this suggestion. We have split this paragraph exactly. This separation improves readability and logical flow between descriptive and quantitative content.\n10. Minor comments: Page 4, Line 99: The value of 21 days is unclear—does it refer to an annual average? Instead of a single average for the 1981-2020 period, it would be more informative to show the evolution of this value over time. Additionally, the sentence states that the year with the highest number of days had 37 days, but which year was this? This information is crucial. If this year is recent, it may also be useful to discuss how many of such years occurred in the last decade, for example.\nResponse:\nWe fully agree with the reviewer that the previously reported number of “21 days” (mean global number of extreme heat stress days) and “37 days” (maximum value) lacks clarity and contextual depth, especially without specifying the reference year.\nRather than providing static average values, which may obscure interannual variability and long-term dynamics, we have revised this section to focus exclusively on the temporal trends and spatial evolution of extreme heat stress days over time.\nAccordingly, we have removed the sentence that referenced the global average of 21 days and the peak year value of 37 days. In its place, we now emphasize the rate of change in extreme heat stress days across decades and across regions, which aligns more closely with the study’s objective to quantify and attribute changes in heat stress intensity and frequency. This also allows us to avoid misinterpretations related to year-to-year variability and better supports the identification of long-term climate change signals.\n11. Minor comments: Page 5, Line 130: The statement that the anthropogenic impact on heat stress is much greater than that caused by natural climate change lacks specificity. How much greater is it? This is a crucial piece of information that needs to be quantified and supported with evidence.\nResponse:\nWe thank the reviewer for highlighting this important point. To address the concern, we have revised the relevant paragraph to quantitatively compare the influence of anthropogenic versus natural climate change on heat stress.\nSpecifically, we now report that:\nFurthermore, the impact of anthropogenic climate change on heat stress was found to be much greater than that of natural climate change (Fig. 3). Between 1981 to 2000, trends in global mean heat stress intensity and extreme heat stress days due to anthropogenic climate change were 0.012°C·yr-1 and 0.267 days·yr-1, respectively, which were 0.017°C-yr-1 and 0.302 days-yr-1 higher than the trends in mean heat stress changes under natural climate change, respectively (Figs. 3c and 3i). These trends increased to 0.015°C·yr-1 and 0.491 days·yr-1, respectively, during the period 2001 to 2020, which were 0.014°C-yr-1 and 0.316 days-yr-1, respectively, higher than the average heat stress trend under natural climate change (Figs. 3f and 3l). (Figs. 3f and 3l).\nThese comparisons are now explicitly presented in the Results section to ensure transparency and provide clear numerical evidence supporting our assertion that anthropogenic forcing is the dominant driver of observed increases in global heat stress.\n12. Minor comments: Page 6, Line 166: Consider breaking the paragraph at \"The Inequality Effect\" to improve readability and structure.\nResponse:\nWe sincerely thank the reviewer for this suggestion. We have split this paragraph exactly. This separation improves readability and logical flow between descriptive and quantitative content.\n13. Minor comments: General: In several parts of the text, the term \"Inequality\" is used without specifying the type of inequality being discussed. It is recommended to always specify \"Regional Inequality\" for clarity.\nResponse:\nWe have replaced ambiguous uses of “inequality” with “regional inequality” everywhere in the text, section headings, and figure captions. For example, Section “Inequality of Global Heat Stress.” is now titled “Regional Inequality of Global Heat Stress”.\n14. Minor comments: Page 6, Line 204: The rates of increase presented from this line need confidence intervals or uncertainties associated with them. Without these, it is difficult to make meaningful comparisons between the values.\nResponse:\nWe thank the reviewer for this important observation. In the revised manuscript, the discussion of these rates has been moved from the original Discussion section to the Results section to align better with the structure and flow of the findings.\nWe evaluated the statistical significance of all reported trends using the non-parametric Mann–Kendall test (p < 0.05) in conjunction with Theil–Sen slope estimators, which provide robust median-based trend magnitudes. This combination is widely adopted in climate trend detection studies due to its resistance to outliers and skewed distributions.\nSpecifically, we now report that:\nWe quantified the mean heat stress intensity and extreme heat stress days using UTCI data, applying the Theil–Sen estimator combined with the Mann-Kendall trend test to calculate global heat stress trends from 1981 to 2020 (p < 0.05) (Table 1).\n15. Minor comments: End of Chapter \"Significant Impacts of Anthropogenic Climate Change\": It would be important to contextualize other studies addressing regional inequalities related to other extremes, as presented in IPCC reports, for example. Given that events are currently occurring in combination and resulting in cascading disasters, this context helps to understand the vulnerability of these regions not only to heat stress but to climate change as a whole.\nResponse:\nWe sincerely thank the reviewer for this insightful suggestion. In response, we have expanded the discussion by explicitly situating our findings on heat stress inequality within the broader context of compound and cascading climate risks, as outlined in IPCC AR6 (2022) and other recent literature. We now highlight how regions such as South Asia and West Africa are exposed to overlapping hazards—e.g., heatwaves coinciding with extreme rainfall, drought, or wildfire—which amplify vulnerability in settings with weak infrastructure and limited adaptive capacity.\nTo support this contextualization, we have incorporated references to recent studies that document regional exposure to compound extremes, and we emphasize the importance of interpreting heat stress inequality within a multi-hazard risk framework. These additions reinforce the relevance of our findings to climate justice, adaptation policy, and multi-dimensional vulnerability assessments. We hope this enhanced framing addresses the reviewer’s request.\nSpecifically, we now report that:\nMoreover, in line with recent IPCC AR6 findings 35, it is increasingly evident that heat stress does not operate in isolation. Regions facing rapid warming also experience compound and cascading climate risks, such as the co-occurrence of heatwaves with drought, wildfire, or extreme rainfall. These intersecting hazards, often reinforced by weak governance or infrastructure, intensify vulnerability across multiple sectors. For instance, South Asia and West Africa have reported simultaneous exposure to heat extremes and rainfall anomalies that overwhelm coping systems 36,37. Understanding regional inequality in heat stress must therefore be placed within a broader multi-hazard perspective that reflects the full spectrum of climate-related threats.\n16. Minor comments: Page 16, Methodology: Why were the Era 5 data limited to years until the year 2020? The phenomenon being studied has intensified in recent years, and 2023 was the hottest year on record. We expect this analysis to be extended to include more recent years.\nResponse:\nWe sincerely thank the reviewer for this important suggestion. We now clarify that while meteorological data are available post-2020 (Era 5), DAMIP model outputs currently end in 2020, which limits our attribution timeframe. We acknowledge this as a limitation and suggest extending analysis to 2021–2023 as future CMIP6 scenarios become available.\n17. Minor comments: Page 357-358: A reference for the models LightGBM and GAM is missing. Please include appropriate citations for these models.\nResponse:\nTo address this, we have expanded Section “Climate-heat stress modeling”:\nGAM Removed: In the revised manuscript, we have removed GAM entirely, as it did not yield additional insights beyond LightGBM and was not used in the final analysis.\nLightGBM Justification: We selected LightGBM—a histogram-based Gradient Boosting Decision Tree implementation—because it delivers state-of-the-art accuracy, exceptional training speed (up to 20× faster than traditional GBDT), and efficient handling of large datasets with high feature dimensions.\nSpecifically, we now report that:\nTo relate climate trends to changes in heat stress, we employed a Light Gradient Boosting Machine (LightGBM) model. We selected LightGBM—a histogram‑based Gradient Boosting Decision Tree implementation—because it delivers state‑of‑the‑art accuracy, exceptional training speed (up to 20× faster than traditional GBDT) 32, and efficient handling of large datasets with high feature dimensions49. Key Formula (Boosting Framework):\n𝐾\n𝐹 (𝑥)=𝐹 (𝑥)+𝜈∑ 𝛾 {𝑥 ∈𝑅 }\n𝑚 𝑚−1 𝑚𝑘 𝑚𝑘\n𝑘=1\nwhere 𝐹 is the ensemble prediction after 𝑚 trees, 𝜈 is the learning rate, 𝛾 are leaf\n𝑚 𝑚𝑘\nweights, and 𝑅 are the partition regions determined by the 𝑚-th tree. This formulation underlies\n𝑚𝑘\nLightGBM’s split-wise (leaf-wise) tree growth strategy.\nThese additions ensure methodological transparency and reproducibility.\n18. Minor comments: Page 431: What is the source and year of the GDP per capita data for the countries?\nResponse:\nThank you for pointing out the need to specify which countries fall into the low- and middle-income categories. We have added the following clarification to the end of the Introduction:\nAdditionally, we evaluate how these contributions vary across regions categorized by the World Bank into high-income (H), upper-middle-income (UM), lower-middle-income (LM), and low-income (L) groups 14.\nA new reference has been added:\nWorld Development Indicators. https://databank.worldbank.org/source/world-development-indicators.\nWe trust this makes our country-group definitions fully transparent.\nReviewer #3:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe greatly appreciate your and your co-reviewer’s engagement with our work. Thank you for investing your time in this review process.\nDear Reviewers,\nThanks for your comments concerning our manuscript entitled “Rising heat, rising inequality: Anthropogenic climate change’s impact on global heat stress”. (No.: NCOMMS-25-02700-T). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the response to your comments are as follows:\nReviewer #4:\nThank you for providing the manuscript. The article presents an analysis of how heat stress trends are experienced unequally by countries at different levels of socioeconomic development. Additionally, it distinguishes the contributions of natural and anthropogenic warming, which adds insight to the discussion.\nHowever, the validity of the results heavily depends on how the contributions to heat stress are decomposed into natural and anthropogenic warming. Unfortunately, the authors do not clearly explain the methodology behind this decomposition. To ensure that readers can fully assess the results, I hope the authors can clarify the following questions.\nResponse:\nWe thank the reviewer for emphasizing the critical importance of transparently describing our decomposition of natural vs. anthropogenic contributions to heat stress.\nIn light of your comments, we have substantially clarified and expanded our methodology. We trust that these additions make our decomposition fully transparent and reproducible.\nSpecifically, we now report that:\nTo distinguish the respective contributions of natural and anthropogenic climate change to heat stress trends, we employed a counterfactual analysis framework based on bias-corrected model data. Specifically, three climate scenarios as follows 43,51:\nHistorical (factual) scenario (ALL): Includes both natural and anthropogenic forcing, derived from reanalysis (ERA5) and historical CMIP6 ALL-forcing simulations.\nNatural-only scenario (NAT): Includes only natural external forcing (e.g., solar and volcanic), derived from CMIP6 DAMIP NAT-forcing experiments.\nAnthropogenic-only scenario (Anthro): Estimated by removing the NAT component from the ALL component, representing the climate influence attributable solely to human activities.\nTo construct the NAT-only (counterfactual) climate series, we applied a delta bias correction approach. Specifically, we calculated temperature and relative humidity under the NAT scenario using the following expressions:\n𝑇 (𝑡)=𝑇 (𝑡)−[𝑇 (𝑡)−𝑇 (𝑡)]\nNoAnthro Obs ALL NAT\n𝑅𝐻 (𝑡)=𝑅𝐻 (𝑡)−[𝑅𝐻 (𝑡)−𝑅𝐻 (𝑡)]\nNoAnthro Obs ALL NAT\nWhere, 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are the temperature and relative humidity series\nNoAnthro NoAnthro\nunder natural-only forcing at time 𝑡 ; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are observed daily values from\nObs Obs\nERA5 reanalysis; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean historical simulations (ALL-\nALL ALL\nforcing); 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean NAT-only simulations from DAMIP models.\nNAT NAT\nWe used the ensemble average of the nine DAMIP models listed in Table 1, all interpolated to a common 0.25° resolution. The resulting counterfactual climate series represent a plausible world in which anthropogenic climate change did not occur.\nThese NAT-only time series were not used to recalculate synthetic UTCI directly (due to the unavailability of global wind and radiation data in NAT-forcing simulations), but were instead fed into our LightGBM model, which had been trained on ERA5-based factual climate–heat stress relationships. This method ensures internal consistency across factual and counterfactual scenarios and avoids introducing additional uncertainty from partial UTCI inputs.\n01. First, the manuscript mentions that 9 climate models were selected to generate historical and natural-forcing historical simulations. Could the authors specify which models were used? Additionally, how many ensemble members from each model were included in the analysis?\nResponse:\nThank you for this useful suggestion. We have included Table 1 in the revised “Data preprocessing” subsection, listing all nine CMIP6 DAMIP models, their ensemble member (r1i1p1f1), time periods, and spatial resolutions. This clarifies the composition of the model ensemble used for attribution analysis.\nSpecifically, we now report that:\nTable 2. Climate models within DAMIP used.\nEnsemble\nHistorical Natural-Forced\nModel Members Resolution\nSimulation Simulation\nUsed\nACCESS-ESM1-5 r1i1p1f1 1961–2014 1961–2020 1.875°×1.25°\nBCC-CSM2-MR r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nCanESM5 r1i1p1f1 1961–2014 1961–2020 2.8°×2.8°\nFGOALS-g3 r1i1p1f1 1961–2014 1961–2020 2.25°×2°\nGFDL-ESM4 r1i1p1f1 1961–2014 1961–2020 1.25°×1°\nIPSL-CM6A-LR r1i1p1f1 1961–2014 1961–2020 2.5°×1.25°\nMIROC6 r1i1p1f1 1961–2014 1961–2020 1.4°×1.4°\nMRI-ESM2-0 r1i1p1f1 1961–2014 1961–2020 1.125°×1.125°\nNorESM2-LM r1i1p1f1 1961–2014 1961–2020 2.5°×1.875°\n02. Second, the authors state that they used the “Delta method.” I assume this refers to \"quantile delta mapping\" as proposed by Cannon et al. (2015). If so, what type of distributions were applied? Did the authors construct temperature and humidity distributions at each grid cell before applying quantile delta mapping, or did they aggregate global temperature and humidity distributions before applying the method?\nResponse:\nThank you for this important question. In our study, we applied a simplified form of the delta bias correction approach, which differs from the Quantile Delta Mapping (QDM) technique proposed by Cannon et al. (2015). While QDM performs quantile-based adjustments and maintains the entire distribution of climate variables, our method focuses on correcting the mean bias in model-simulated temperature and relative humidity by subtracting the difference between ALL-forcing and NAT-only ensemble means from observed values at each grid cell.\nSpecifically, for each 0.25° grid cell, we computed:\n𝑇 (𝑡) = 𝑇 (𝑡)−[𝑇 (𝑡)−𝑇 (𝑡)]\nNoAnthro Obs ALL NAT\n𝑅𝐻 (𝑡) = 𝑅𝐻 (𝑡)−[𝑅𝐻 (𝑡)−𝑅𝐻 (𝑡)]\nNoAnthro Obs ALL NAT\nThis “delta bias correction” method enables us to preserve the observed temporal structure and variability while approximating a counterfactual climate that excludes anthropogenic influence. We have clarified this terminology and approach in the revised Methods section.\nSpecifically, we now report that:\nTo distinguish the respective contributions of natural and anthropogenic climate change to heat stress trends, we employed a counterfactual analysis framework based on bias-corrected model data. Specifically, three climate scenarios as follows 43,51:\nHistorical (factual) scenario (ALL): Includes both natural and anthropogenic forcing, derived from reanalysis (ERA5) and historical CMIP6 ALL-forcing simulations.\nNatural-only scenario (NAT): Includes only natural external forcing (e.g., solar and volcanic), derived from CMIP6 DAMIP NAT-forcing experiments.\nAnthropogenic-only scenario (Anthro): Estimated by removing the NAT component from the ALL component, representing the climate influence attributable solely to human activities.\nTo construct the NAT-only (counterfactual) climate series, we applied a delta bias correction approach. Specifically, we calculated temperature and relative humidity under the NAT scenario using the following expressions:\n𝑇 (𝑡)=𝑇 (𝑡)−[𝑇 (𝑡)−𝑇 (𝑡)]\nNoAnthro Obs ALL NAT\n𝑅𝐻 (𝑡)=𝑅𝐻 (𝑡)−[𝑅𝐻 (𝑡)−𝑅𝐻 (𝑡)]\nNoAnthro Obs ALL NAT\nWhere, 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are the temperature and relative humidity series\nNoAnthro NoAnthro\nunder natural-only forcing at time 𝑡 ; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are observed daily values from\nObs Obs\nERA5 reanalysis; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean historical simulations (ALL-\nALL ALL\nforcing); 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean NAT-only simulations from DAMIP models.\nNAT NAT\nWe used the ensemble average of the nine DAMIP models listed in Table 1, all interpolated to a common 0.25° resolution. The resulting counterfactual climate series represent a plausible world in which anthropogenic climate change did not occur.\nThese NAT-only time series were not used to recalculate synthetic UTCI directly (due to the unavailability of global wind and radiation data in NAT-forcing simulations), but were instead fed into our LightGBM model, which had been trained on ERA5-based factual climate–heat stress relationships. This method ensures internal consistency across factual and counterfactual scenarios and avoids introducing additional uncertainty from partial UTCI inputs.\n03. After applying quantile delta mapping, the authors would obtain temperature and humidity distributions (as well as time series) for the synthesized natural-forcing historical period. However, how were the corresponding UTCI values for this scenario derived? Did the authors simply plug in temperature and humidity values into the UTCI calculator? If so, how were wind speed and radiative temperature handled? Or, was there a different method used to compute UTCI for the natural-forcing scenario? This distinction is important because the manuscript states that UTCI for the all-forcing scenario was directly obtain from the ERA5 dataset, but the approach for the natural-forcing scenario seems to be different.\nResponse:\nWe appreciate this careful observation. As correctly noted, the UTCI values for the all-forcing (historical) scenario were directly obtained from ERA5-HEAT reanalysis.\nHowever, for the NAT-only scenario, we did not attempt to re-calculate UTCI directly due to the lack of wind speed and radiation variables in the CMIP6 DAMIP NAT-forcing simulations. Instead, we generated NAT-only temperature and humidity time series via delta bias correction (as described above), and then used these as inputs to a LightGBM model trained on the ERA5 dataset. This model had learned the empirical relationship between UTCI trends and the available climatic variables.\nThis approach ensured internal consistency between the factual and counterfactual scenarios while avoiding the introduction of uncertainty from missing inputs in synthetic UTCI calculation. We have clarified this workflow in the revised methodology.\nSpecifically, we now report that:\nTo distinguish the respective contributions of natural and anthropogenic climate change to heat stress trends, we employed a counterfactual analysis framework based on bias-corrected model data. Specifically, three climate scenarios as follows 43,51:\nHistorical (factual) scenario (ALL): Includes both natural and anthropogenic forcing, derived from reanalysis (ERA5) and historical CMIP6 ALL-forcing simulations.\nNatural-only scenario (NAT): Includes only natural external forcing (e.g., solar and volcanic), derived from CMIP6 DAMIP NAT-forcing experiments.\nAnthropogenic-only scenario (Anthro): Estimated by removing the NAT component from the ALL component, representing the climate influence attributable solely to human activities.\nTo construct the NAT-only (counterfactual) climate series, we applied a delta bias correction approach. Specifically, we calculated temperature and relative humidity under the NAT scenario using the following expressions:\n𝑇 (𝑡)=𝑇 (𝑡)−[𝑇 (𝑡)−𝑇 (𝑡)]\nNoAnthro Obs ALL NAT\n𝑅𝐻 (𝑡)=𝑅𝐻 (𝑡)−[𝑅𝐻 (𝑡)−𝑅𝐻 (𝑡)]\nNoAnthro Obs ALL NAT\nWhere, 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are the temperature and relative humidity series\nNoAnthro NoAnthro\nunder natural-only forcing at time 𝑡 ; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are observed daily values from\nObs Obs\nERA5 reanalysis; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean historical simulations (ALL-\nALL ALL\nforcing); 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean NAT-only simulations from DAMIP models.\nNAT NAT\nWe used the ensemble average of the nine DAMIP models listed in Table 1, all interpolated to a common 0.25° resolution. The resulting counterfactual climate series represent a plausible world in which anthropogenic climate change did not occur.\nThese NAT-only time series were not used to recalculate synthetic UTCI directly (due to the unavailability of global wind and radiation data in NAT-forcing simulations), but were instead fed into our LightGBM model, which had been trained on ERA5-based factual climate–heat stress relationships. This method ensures internal consistency across factual and counterfactual scenarios and avoids introducing additional uncertainty from partial UTCI inputs.\n04. After applying the quantile delta mapping, the authors would obtain temperature/humidity distributions (and time series) for the synthesized natural-forcing historical period. But how do the authors get the synthesized UTCI values for this natural-forcing scenario? Do they simply plug in the temperature/humidity? What about the wind speed and radiating temperature? Or do the authors use different method to calculate the UTCI for the natural-forcing scenario? I asked this question because the authors used the computed UTCI from EAR5 for the all-forcing scenario, but seemed to use different method to obtain the UTCI for the natural-forcing scenario.\nResponse:\nWe appreciate this careful observation. As correctly noted, the UTCI values for the all-forcing (historical) scenario were directly obtained from ERA5-HEAT reanalysis.\nHowever, for the NAT-only scenario, we did not attempt to re-calculate UTCI directly due to the lack of wind speed and radiation variables in the CMIP6 DAMIP NAT-forcing simulations. Instead, we generated NAT-only temperature and humidity time series via delta bias correction (as described above), and then used these as inputs to a LightGBM model trained on the ERA5 dataset. This model had learned the empirical relationship between UTCI trends and the available climatic variables.\nThis approach ensured internal consistency between the factual and counterfactual scenarios while avoiding the introduction of uncertainty from missing inputs in synthetic UTCI calculation. We have clarified this workflow in the revised methodology.\nSpecifically, we now report that:\nTo distinguish the respective contributions of natural and anthropogenic climate change to heat stress trends, we employed a counterfactual analysis framework based on bias-corrected model data. Specifically, three climate scenarios as follows 43,51:\nHistorical (factual) scenario (ALL): Includes both natural and anthropogenic forcing, derived from reanalysis (ERA5) and historical CMIP6 ALL-forcing simulations.\nNatural-only scenario (NAT): Includes only natural external forcing (e.g., solar and volcanic), derived from CMIP6 DAMIP NAT-forcing experiments.\nAnthropogenic-only scenario (Anthro): Estimated by removing the NAT component from the ALL component, representing the climate influence attributable solely to human activities.\nTo construct the NAT-only (counterfactual) climate series, we applied a delta bias correction approach. Specifically, we calculated temperature and relative humidity under the NAT scenario using the following expressions:\n𝑇 (𝑡)=𝑇 (𝑡)−[𝑇 (𝑡)−𝑇 (𝑡)]\nNoAnthro Obs ALL NAT\n𝑅𝐻 (𝑡)=𝑅𝐻 (𝑡)−[𝑅𝐻 (𝑡)−𝑅𝐻 (𝑡)]\nNoAnthro Obs ALL NAT\nWhere, 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are the temperature and relative humidity series\nNoAnthro NoAnthro\nunder natural-only forcing at time 𝑡 ; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are observed daily values from\nObs Obs\nERA5 reanalysis; 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean historical simulations (ALL-\nALL ALL\nforcing); 𝑇 (𝑡) and 𝑅𝐻 (𝑡) are ensemble mean NAT-only simulations from DAMIP models.\nNAT NAT\nWe used the ensemble average of the nine DAMIP models listed in Table 1, all interpolated to a common 0.25° resolution. The resulting counterfactual climate series represent a plausible world in which anthropogenic climate change did not occur.\nThese NAT-only time series were not used to recalculate synthetic UTCI directly (due to the unavailability of global wind and radiation data in NAT-forcing simulations), but were instead fed into our LightGBM model, which had been trained on ERA5-based factual climate–heat stress relationships. This method ensures internal consistency across factual and counterfactual scenarios and avoids introducing additional uncertainty from partial UTCI inputs.\n05. While some of these questions may seem straightforward, a full disclosure of the methodology is essential to ensure the robustness of the results. Given the significance of the findings, I encourage the authors to provide a more detailed explanation of their approach.\nResponse:\nWe fully agree with the reviewer that methodological clarity is essential. In the revised manuscript, we have provided a detailed and transparent explanation of our bias correction procedure, including:\n The definition and rationale for using a “delta bias correction” approach;\n The grid-cell-based implementation of temperature and humidity adjustment;\n The use of LightGBM trained on ERA5 data to predict UTCI for the NAT-only climate;\nThese additions strengthen the reproducibility and credibility of our counterfactual heat stress analysis.\nDear Reviewers:\nThanks for your comments concerning our manuscript entitled “Rising heat, rising regional inequality: Anthropogenic climate change’s impact on global heat stress”. (No.: NCOMMS-25-02700A). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red in the paper. The main corrections in the paper and the response to your comments are as follows:\nReviewer #1:\nThanks for your comments on our paper. We have revised our paper according to your comments:\nReview: The authors investigated the connection between climate change and thermal comfort in economically disadvantaged areas, successfully addressing a critical challenge in distinguishing the anthropogenic and natural contributions to climate change.\nThis is an engaging and timely article that explores topics of significant importance, particularly the interplay between anthropogenic and natural factors contributing to climate change, with a focus on low- and middle-income countries. The exploration of thermal comfort as a related factor is particularly noteworthy, as it remains underexplored, especially in more economically deprived regions. Additionally, the use of open-source data is commendable and aligns with best practices for transparency and reproducibility in research.\nHowever, there are several areas requiring improvement, particularly in terms of methodological clarity and the discussion of related literature, which l will detail below.\nWhile the research demonstrates potential for publication, I do not recommend it in its current form due to these substantive concerns.\nResponse:\nWe sincerely appreciate your overall positive assessment of our manuscript and its contributions, particularly our effort to disentangle anthropogenic and natural signals of climate change and to examine thermal comfort in economically disadvantaged regions using open-source data.\nWe acknowledge your call for improved methodological clarity and more extensive engagement with relevant literature. As detailed in our itemized responses below, we have addressed each of the five specific suggestions, including revisions to abbreviation use, figure readability, the addition of region-level statistics, clarification of thermal comfort terminology, and expanded discussion of the disproportionate anthropogenic heat signal in lower-income countries.\n01. Abbreviations: The manuscript contains several instances of duplicated or delayed abbreviation definitions. For example: UTCI is correctly defined in the abstract but is unnecessarily redefined in lines 49, 74, and 280; LightGBM is first introduced in line 77, yet it is redefined again in lines 127, 284, and 353; CMIP and DAMIP are mentioned in line 288 but are not defined until lines 310 and 311; NAT is only explained in line 373, despite being introduced earlier in the manuscript. I recommend a careful review to ensure that abbreviations are defined only once—at their first mention—and used consistently thereafter. This will improve clarity and eliminate redundancy.\nResponse:\nWe thank you for carefully pointing out the duplicated and delayed abbreviation definitions throughout the manuscript. In response, we have thoroughly reviewed and revised the manuscript to ensure that:\n Each abbreviation is defined only once at its first mention;\n All subsequent uses employ the abbreviation consistently;\n Any delayed definitions have been moved forward to the appropriate location of first use;\n Any redundant redefinitions have been removed to enhance clarity and reduce unnecessary repetition.\nSpecific changes include:\n(1) UTCI: Defined once in the Abstract and again at its first mention in the Introduction. Redundant definitions have been deleted.\n(2) LightGBM: Now defined only at first use; later redefinitions have been removed.\n(3) CMIP and DAMIP: Their definitions have been moved to their first appearance, eliminating delayed definitions.\n(4) NAT: Definition has been moved up from line 373 to the first mention of the term.\n02. Figures: The size of figures 3 and 4 should be increased. As it is they are difficult to read.\nResponse:\nWe appreciate your helpful suggestion regarding figure readability.\nFigure 3: We enlarged all font sizes used in annotations to improve clarity. In addition, we removed a bar chart that contained relatively low-value information and instead integrated those values directly into the main text. This revision streamlines the visual presentation and improves focus.\nFigure 4: Similarly, we increased the font sizes of all annotations to ensure clear visibility and legibility.\nSpecifically, we now report that:\nFig. 3. Trends in mean heat stress intensity and extreme heat stress days under natural and anthropogenic climate change: (a)-(b) trend of mean heat stress intensity under natural and anthropogenic climate change, 1981-2000; (c)-(d) trend of mean heat stress intensity under natural and anthropogenic climate change, 2001-2020; (e)-(f) trend of extreme heat stress days under natural and anthropogenic climate change, 1981-2000; (g)-(h) trend of extreme heat stress days under natural and anthropogenic climate change, 2001-2020.\nFig. 4. Distribution and area ratio of the types of changes in the global mean heat stress intensity and extreme heat stress days. The bars indicate the area ratio of each type. (a) and (b) Changes in mean heat stress intensity and changes in extreme heat stress days, 1981-2000; (c) and (d) Changes in mean heat stress intensity and changes in extreme heat stress days, 2001-2020.\n03. In the results, I believe it would be interesting to further explore the trends of Mean heat stress intensity and Extreme heat stress days results, per continent (or other sub-geographical divisions), if possible. This would significantly improve the results section. So, table 1 would be as it is + geographical trends.\nResponse:\nWe greatly appreciate your insightful suggestion to expand the geographical scope of the results.\nIn response, we conducted additional quantitative analyses at the sub-continental level. Specifically, we compiled:\nTable S1: Trends in mean heat stress intensity (°C·yr-1) across global regions.\nTable S2: Trends in extreme heat stress days (days·yr-1) across global regions.\nBoth tables present changes over two periods (1981–2000 and 2001–2020), offering a more detailed breakdown of regional dynamics. In the main text, we have added a paragraph summarizing key findings from these tables, such as notable increases in parts of South America, North America, South Asia, and sub-Saharan Africa.\nSpecifically, we now report that:\nComparison of trends in mean heat stress intensity over time across regions (Fig. 1d and Table S1). At the regional scale, a total of 37 regions had higher trend means for 2001-2020 compared to 1981-2000. Eastern and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trend increases for 2001-2020 of 0.081°C·yr-1 and 0.071°C·yr-1 relative to 1981–2000, respectively. Comparable surges are evident in western Southern Africa (trend rising to 0.031 °C·yr-1 in 2001–2020) and eastern Southern Africa (from -0.006°C·yr-1 to 0.021°C·yr-1). In north-eastern South America the trend accelerated from 0.011°C·yr-1 to 0.030°C·yr-1, and in north-western South America from 0.001°C·yr-1 to 0.021°C·yr-1, underscoring that Africa and South America are experiencing increases on par with, or exceeding, those in Australia.\nComparison of trends in extreme heat stress days across regions over time (Fig. 2d and Table S2). At the regional scale, a total of 44 regions had higher trend means for 2001-2020 compared to 1981-2000. Northern and central Australia lead, with trend increases of 2.247 days·yr-1 and 2.030 days·yr-1 for 2001-2020 relative to 1981-2000, respectively. The low-latitude tropics record similarly steep rises: Central Africa now reaches +2.299 days·yr-1, North-Eastern Africa +1.834 days·yr-1, Eastern Southern Africa +1.576 days·yr-1, and Madagascar +1.586 days·yr-1. Equally pronounced surges are observed across tropical South America. The North South America sub-region shows an increment of +1.991 days·yr-1, while the South American Monsoon zone rises by +1.863 days·yr-1, and the North-Eastern South America sector by +1.692 days·yr-1. These values rival or exceed those of Australia, confirming that the rapid escalation in extreme heat stress days is also occurring across equatorial Africa as well as tropical South America.\n04. In the discussion (line 200) the reader is introduced to “perceived” concept which is different from what the authors are investigating. Thermal comfort perception entails subjective conditions to understand thermal comfort other than just heat budget models such as the UTCI which considers meteorological, metabolic, physiological and clothing conditions.\nFor further understanding, I suggest some papers in which the definitions within the study of thermal comfort are clearly given.\nFor the perception and other subjective fields you can find the definitions here: https://doi.org/10.1007/s44327-024-00020-8.\nFor what the authors are doing, which is researching about thermophysiological comfort which is how the body reacts to certain environmental conditions, I suggest this paper: https://doi.org/10.1016/j.uclim.2024.101933. I either suggested the authors to remove the perception component of thermal comfort and leave it as human thermal comfort or just change to human-thermophysiological comfort.\nResponse:\nWe sincerely thank you for highlighting the mischaracterization regarding the term “perceived thermal comfort”.\nAs you correctly noted, our study does not investigate subjective thermal perception or self-reported comfort levels. Instead, it relies on heat budget models (UTCI), which integrates meteorological and physiological variables to quantify thermophysiological responses to environmental heat stress.\nIn response to your suggestion, we have removed the term “perceived” and revised all related language to refer to either: human thermal comfort. We have also cited the two references you recommended to clarify the distinction between perceived (subjective) and thermophysiological (objective) thermal comfort.\n05. In the discussion, I would like the authors to delve into the reasons why maybe low or low-medium income countries have higher influence of anthropogenic induced heat than other regions. Is it the lack of investment in environmental policies and environmental education? This could make a useful reflection since the authors mentioned in the introduction that this research could be useful for policy-making information.\nResponse:\nWe sincerely thank you for this thoughtful suggestion. We agree that the observation of higher anthropogenic contributions to heat stress in low- and lower-middle-income countries warrants deeper discussion, both as a scientific insight and as a foundation for policy implications, especially in the context of unequal adaptive capacities. In response, we have added a new paragraph in the Discussion section that explores potential mechanisms behind this phenomenon. Specifically, we discuss:\n(1) Limited investments in environmental policy and climate-adaptive urban planning;\n(2) Insufficient natural buffers, such as urban green spaces and vegetated surfaces;\n(3) Higher proportions of outdoor labor and inadequate cooling infrastructure;\n(4) Rapid urban expansion where anthropogenic signals emerge more distinctly, with less masking by natural variability compared to high-income regions.\nSpecifically, we now report that:\nMany low-income and lower-middle-income countries exhibit a higher anthropogenic contribution to heat stress (e.g., sub-Saharan African countries), possibly because the signal of anthropogenic climate change is less masked by adaptation buffers. In high-income countries, widespread access to cooling, health infrastructure, and green space mitigates the realized impacts of rising heat stress. In contrast, low-income countries lack such protections, urban areas often expand rapidly without climate-resilient planning, and labor forces are concentrated in climate-sensitive outdoor sectors. These vulnerabilities allow the anthropogenic signal to manifest more directly and intensely.\nReviewer #2:\nThanks for your comments on our paper. We have revised our paper according to your comments:\n01. Thank you for the revisions — we appreciate the improved effort to present results in a broader regional context. However, we still find that the quantitative focus remains disproportionately centered on Australia. Although Figures 1 and 2 clearly show that regions in South America (especially Brazil) and parts of Africa are among the most affected, these areas receive much less detailed attention in the text.\nResponse:\nWe sincerely appreciate your emphasis on ensuring regional balance in the interpretation of our findings. We fully agree that while Figures 1 and 2 highlights that South America (especially Brazil) and parts of Africa are among the most severely affected by increasing heat stress, our previous version placed disproportionate emphasis on Australia in the textual discussion. In response, we have revised the Results section to include additional quantitative descriptions for Brazil and key regions in sub-Saharan Africa.\nSpecifically, we now report that:\nAt the regional scale, a total of 37 regions had higher trend means for 2001-2020 compared to 1981-2000. Eastern and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trend increases for 2001-2020 of 0.081°C·yr-1 and 0.071°C·yr-1 relative to 1981–2000, respectively. Comparable surges are evident in western Southern Africa (trend rising to 0.031 °C·yr-1 in 2001–2020) and eastern Southern Africa (from -0.006°C·yr-1 to 0.021°C·yr-1). In north-eastern South America the trend accelerated from 0.011°C·yr-1 to 0.030°C·yr-1, and in north-western South America from 0.001°C·yr-1 to 0.021°C·yr-1, underscoring that Africa and South America are experiencing increases on par with, or exceeding, those in Australia.\nAt the regional scale, a total of 44 regions had higher trend means for 2001-2020 compared to 1981-2000. Northern and central Australia lead, with trend increases of 2.247 days·yr-1 and 2.030 days·yr-1 for 2001-2020 relative to 1981-2000, respectively. The low-latitude tropics record similarly steep rises: Central Africa now reaches +2.299 days·yr-1, North-Eastern Africa +1.834 days·yr-1, Eastern Southern Africa +1.576 days·yr-1, and Madagascar +1.586 days·yr-1. Equally pronounced surges are observed across tropical South America. The North South America sub-region shows an increment of +1.991 days·yr-1, while the South American Monsoon zone rises by +1.863 days·yr-1, and the North-Eastern South America sector by +1.692 days·yr-1. These values rival or exceed those of Australia, confirming that the rapid escalation in extreme heat stress days is also occurring across equatorial Africa as well as tropical South America.\n02. While Australia is described with specific quantitative trends, other highly impacted regions are only referenced in general qualitative terms. We suggest including similarly detailed quantitative information for these other regions — particularly South America and Africa — to better reflect the global nature of the study.\nResponse:\nWe greatly appreciate your thoughtful suggestion to include similarly detailed quantitative information for other regions, particularly South America and Africa, to better reflect the study’s global relevance.\nIn response, we have now quantified and reported the trends for 44 sub-continental regions worldwide, including multiple regions across South America (e.g., Eastern and Northern South America) and Africa (e.g., Central, Western, and Southern Africa). These results are presented in Supplementary Tables S1 and S2, which document the trends in mean heat stress intensity (°C·yr-1) and extreme heat stress days (days·yr-1) across global regions, respectively.\nSpecifically, we now report that:\nComparison of trends in mean heat stress intensity over time across regions (Fig. 1d and Table S1). At the regional scale, a total of 37 regions had higher trend means for 2001-2020 compared to 1981-2000. Eastern and northern Australia, in particular, experienced the most dramatic increases in mean heat stress intensity, with trend increases for 2001-2020 of 0.081°C·yr-1 and 0.071°C·yr-1 relative to 1981–2000, respectively. Comparable surges are evident in western Southern Africa (trend rising to 0.031 °C·yr-1 in 2001–2020) and eastern Southern Africa (from -0.006°C·yr-1 to 0.021°C·yr-1). In north-eastern South America the trend accelerated from 0.011°C·yr-1 to 0.030°C·yr-1, and in north-western South America from 0.001°C·yr-1 to 0.021°C·yr-1, underscoring that Africa and South America are experiencing increases on par with, or exceeding, those in Australia.\nComparison of trends in extreme heat stress days across regions over time (Fig. 2d and Table S2). At the regional scale, a total of 44 regions had higher trend means for 2001-2020 compared to 1981-2000. Northern and central Australia lead, with trend increases of 2.247 days·yr-1 and 2.030 days·yr-1 for 2001-2020 relative to 1981-2000, respectively. The low-latitude tropics record similarly steep rises: Central Africa now reaches +2.299 days·yr-1, North-Eastern Africa +1.834 days·yr-1, Eastern Southern Africa +1.576 days·yr-1, and Madagascar +1.586 days·yr-1. Equally pronounced surges are observed across tropical South America. The North South America sub-region shows an increment of +1.991 days·yr-1, while the South American Monsoon zone rises by +1.863 days·yr-1, and the North-Eastern South America sector by +1.692 days·yr-1. These values rival or exceed those of Australia, confirming that the rapid escalation in extreme heat stress days is also occurring across equatorial Africa as well as tropical South America.\n03. Moreover, Figure 3 indicates that the anthropogenic contribution to heat stress is substantially higher in Brazil and many parts of Africa than in Australia. This important finding deserves more emphasis and discussion in the text.\nResponse:\nWe appreciate this insightful suggestion. To address it, we have added a new sentence in the Results section to explicitly highlight that regions such as Brazil and many parts of equatorial Africa exhibit particularly high anthropogenic attribution. In addition, we have expanded the Discussion section to reflect on the possible reasons behind this pattern.\nSpecifically, we now report that:\nNotably, the physical area experiencing increases driven by anthropogenic climate change is approximately twice as large as the area affected by natural climate variability and is predominantly located between 30° north and south latitude. Within this latitudinal band, regions such as Brazil and many parts of equatorial Africa exhibit particularly high anthropogenic attribution.\nMany low-income and lower-middle-income countries exhibit a higher anthropogenic contribution to heat stress (e.g., Brazil and sub-Saharan African countries), possibly because the signal of anthropogenic climate change is less masked by adaptation buffers. In high-income countries, widespread access to cooling, health infrastructure, and green space mitigates the realized impacts of rising heat stress. In contrast, low-income countries lack such protections, urban areas often expand rapidly without climate-resilient planning, and labor forces are concentrated in climate-sensitive outdoor sectors. These vulnerabilities allow the anthropogenic signal to manifest more directly and intensely.\n04. In the sentence 'we employ a relative threshold (90th percentile of daily maxima within a 15-day moving window)', please specify whether a single global threshold was used, or whether the threshold was calculated per region or per grid point.\nResponse:\nThank you for this clarification request. In our analysis, the relative threshold was calculated per grid point based on the 90th percentile of daily maximum values within a 15-day moving window over the baseline period. This grid-based approach allows us to account for local climatological conditions. We have revised the relevant sentence in the Methodology section to explicitly state this point.\nSpecifically, we now report that:\nExtreme heat stress days were identified using a relative threshold approach. While the official UTCI classification designates >46 °C as ‘Extreme Heat Stress’, this absolute threshold does not account for local acclimatization or climate variability. Therefore, we employed a grid-specific and time-adaptive relative threshold, defined as the 90th percentile of daily maximum UTCI values calculated within a 15-day moving window .\n05. We recommend avoiding the term 'racial minorities' in the Brazilian context, as Black and Brown people constitute the majority (~55% according to the Brazilian Institute of Geography and Statistics) of the population (unlike in the United States, where the term is more appropriate, for example). Instead, we suggest using terms like 'underrepresented racialized groups' or 'historically marginalized racial groups', which more accurately reflect Brazil's social and structural realities.\nResponse:\nWe appreciate this important observation. In response, we have replaced the term “racial minorities” with “historically marginalized racial groups” throughout the manuscript when referring to the Brazilian context. We thank you for this thoughtful and culturally sensitive recommendation.\nSpecifically, we now report that:\nIn Brazil, studies show that heat-related excess mortality is disproportionately concentrated among older adults, historically marginalized racial groups, and low-education populations, and in Latin American cities, socioeconomic characteristics such as poverty and segregation also modify the impact of heat-related mortality.\nReviewer #3:\nThanks for your comments on our paper. We have revised our paper according to your comments:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe greatly appreciate your and your co-reviewer’s engagement with our work. Thank you for investing your time in this review process.\nReviewer #4:\nThanks for your comments on our paper. We have revised our paper according to your comments:\nThank you for the thorough revisions. I think the manuscript is much improved.\nResponse:\nWe sincerely appreciate your positive feedback and are grateful for your recognition of the improvements made."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors fulfilled the expectations and the paper is, in my opinion, now ready to be published.", "reviewer_2": "I thank the authors for all their efforts in improving the manuscript. In my opinion, this is ready for publication.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter