Global economic exposure to climate change amplified by spatially compounding climate extremes

Domain: chemistry
Published: 2026-03-03
Editorial outcome: minor_revision

Abstract
Despite growing evidence that climate extreme events can significantly affect local economies, the implications of cross-regional and planetary-scale dependencies in climate extremes remain inadequately understood. We demonstrate a crucial link between the projected increase in spatially compounding hot, wet, and dry extremes and the amplification of global economic exposure. Based on Earth System Model projections from the 6th phase of the Coupled Model Intercomparison Project, we analyze how planetary-scale and cross-regional dependencies can exacerbate regional disparities in economic exposure. Our findings reveal that regions with lower present-day economic wealth are more likely to face extreme events simultaneously with other areas, amplifying the potential threats to their economic stability. This study highlights the necessity of considering economic exposure to climate extremes beyond local scales, emphasizing the need for assessing cross-regional exposures and understanding the connections between localized exposures and global economic dynamics.

Introduction
Over the past two decades, weather-related disasters—such as droughts, floods, heatwaves, and storms—have caused annual global gross domestic product (GDP) losses averaging 0.14%1, with 0.05–0.82% of global GDP attributable to anthropogenic climate change2. Storms contributed the largest share of these losses (64%), followed by heatwaves (16%), floods (10%), and droughts (10%)2. In high-income countries more than 80% of weather and climate extremes have resulted in economic losses amounting to less than 0.1% of the respective GDP, with no disaster causing losses exceeding 3.5% of GDP3. In contrast, in low-income countries, several disasters had economic impacts ranging from 5% to 30% of their GDP3, highlighting the significant spatial disparities in the economic risks posed by climate change. Projections based on temperature and precipitation means, extremes, and variability suggest that global aggregate losses could reach 10% of GDP under 3°C warming, with the most severe impacts—up to 17% of GDP—occurring in poorer, low-latitude countries4. Income reductions of up to 19% within the next 26 years are projected, regardless of future emission pathways5. Global economic damages are projected to increase non-linearly with global warming, with significant regional variation in economic losses exacerbating global disparities further4–7. However, recent literature focuses on projections of economic losses due to an increase in intensity and frequency of hot or wet extreme events only. Besides an increase in individual extreme events, the simultaneous occurrence of climate extremes at different locations-referred to as ”spatially compounding climate extremes” - will also likely increase due to global warming8–10.

Although mounting evidence suggests that extreme events impact the economy at both regional and global scales, it is unclear how spatially compounding effects of these events affect economic outcomes. Understanding these dynamics is essential because they can trigger cascading disruptions across geographies and sectors, leading to globally simultaneous impacts11,12. When multiple regions face climate-related disruptions simultaneously, cascading effects can amplify economic instability beyond the sum of individual impacts, posing a systemic risk to the global economy due to the interconnected nature of trade, finance, and supply chains. Such systemic risks are particularly concerning in critical sectors such as food production, where disruptions can escalate global economic instability13. Thus, assessing how one region’s exposure to an extreme event aligns with simultaneous exposures in other regions is crucial for understanding the resilience of the global economy to widespread climate-induced disruptions.

This study demonstrates the relevance of linking the projected increase in spatially compounding climate extremes to changes in economic exposure. We address this question by demonstrating how cross-regional and planetary-scale dependencies may further amplify regional disparities in economic exposure to extreme events. In addition to projections in hot and wet extremes, we also consider economic value affected by water scarcity and soil moisture droughts, which has not yet been analyzed on global scales. Furthermore, we incorporate future GDP projections based on five distinct Shared Socioeconomic Pathway (SSP14) narratives, acknowledging that economic exposure to climate extremes will be shaped not only by climatic changes but also by socioeconomic transformations. To ensure consistency between socioeconomic and climatic assumptions, we link the SSP narratives with their corresponding Representative Concentration Pathways (RCPs15) following the established SSP–RCP framework16. Specifically, we consider five SSP–RCP combinations (SSP1–2.6, SSP2–4.5, SSP3–7.0, SSP4-6.0, and SSP5–8.5), which span a wide range of plausible futures in terms of greenhouse gas forcing and socioeconomic transformation.

Results
Spatially Compounding Economic Exposure Illustrated by Heatwaves

Extreme events - defined as events such as heatwaves that occurred on average every 20 years in a preindustrial climate - are projected to affect more areas concurrently in the future (Fig. 1a). Consequently, the exposure of global annual GDP to such events is expected to increase, with the magnitude of this rise depending on the level of warming. Economic exposure is influenced not only by climate change but also by future socioeconomic transformation. To account for this, we incorporate GDP projections from five SSPs (SSP1–SSP5; Fig. 1b), allowing us to assess how evolving economic conditions influence additional GDP exposure relative to the 2001–2020 baseline under different warming scenarios. For each time step, model, and scenario, we first identify grid cells affected by an extreme event (Fig. 1d) and then extract the corresponding GDP distribution for the same year (Fig. 1e). Intersecting these two layers yields the GDP affected per scenario, model, and time step (Fig. 1f). The exposed GDP is further decomposed into two components: (i) a climate component, representing how exposure evolves due to changes in climate extremes while keeping GDP constant at 2020 levels; and (ii) a socioeconomic component, capturing the influence of GDP growth and distribution.

Global annual GDP affected by Global Concurrent Extremes (GCE; in this case heatwaves; Fig. 2a) is projected to increase markedly under enhanced global warming relative to present-day (2001-2020) conditions (Fig. 2b). In the mid-century period (2041–2060), scenarios with higher projected GDP, such as SSP5 (≈ 231 trillion USD in 2050; Fig. 1b), show the largest additional exposure under SSP5–8.5, amounting to 171 trillion USD (111–220 trillion USD, 5th–95th percentile), corresponding to roughly 45% (25-65%) of mid-century GDP relative to the present-day baseline. In contrast, SSP3, which projects the lowest GDP (≈ 134 trillion USD in 2050), results in 73 trillion USD (45–103 trillion USD) of additional GDP exposed under SSP3–7.0, representing around 37% (19–57%) of mid-century GDP. SSP2, currently considered the most likely scenario (≈ 159 trillion USD in 2050), shows 93 trillion USD (53–131 trillion USD) of additional exposure under RCP4.5, corresponding to approximately 37% (16–58%) of GDP. Across all scenarios, the increasing GDP exposure is largely driven by climate effects, with socio-economic growth contributing only marginally.

In the following, we focus on describing the additional GDP exposure under SSP2–4.5 in the mid-century period, which represents the most likely future development; results for other SSP–RCP scenarios are provided for reference in Supplementary Figures 4–29. We introduce the concept of Regional Concurrent Extremes (RCE), which captures the fraction of regional GDP concurrently affected by extreme events occurring within the same year (Fig. 2c). In the mid-century period under SSP2–4.5, considerable regional disparities are apparent in additional GDP exposure to heatwaves (Fig. 2d). Mid-latitude regions, including the Mediterranean (MED) and Eastern Central Asia (ECA) regions, experience the highest additional exposure, reaching up to 45% (39–52%) of regional GDP relative to present-day conditions. Elevated RCE also affects tropical and subtropical regions, such as the South American Monsoon (SAM) region and southern Africa. While climate-driven changes remain the primary contributor across all regions, GDP growth increasingly aligns with heatwave-prone areas. In East Southern Africa (ESAF), approximately 10% of regional GDP exposure can be attributed to socio-economic factors under SSP2–4.5, with even larger contributions (up to 20%) in Northern Australia (NAU) and Southeast Asia (SEA) (Supplementary Figure 3).

Economic impacts of extreme events are inherently systemic, arising from the interconnectedness of economies through international trade, finance, and supply chains. Thus, analyzing economic exposure to extreme events requires a comprehensive perspective that accounts for both Local effects of Global Concurrent Extremes (L-GCE) and Cross-Regional Concurrent Extremes (C-RCE). Examining local GDP affected in relation to global occurrences (L-GCE; Fig. 3a) provides insights into economic resilience to spatially compounding events, while analyzing affected cross-regional GDP (C-GCE; Fig. 3c) helps disentangle regional interdependencies.

Regions with high L-GCE correspond to those projected to experience strong RCE, including MED, ECA, the Arabian Peninsula (ARPE), as well as tropical regions in South America and Africa (Fig. 3b). Under SSP2–4.5, up to 108 trillion USD (66–152 trillion USD) of global GDP is concurrently affected in the mid-century period. These hotspots are frequently affected by heat extremes concurrently with other regions, reflecting their strong involvement in spatially compounding events.

Regions that are frequently affected by heat extremes concurrently with other parts of the world show high C-RCE values, highlighting the interconnected nature of global exposure (Fig. 3d). ECA, for example, exhibits selective but strong connectivity, with its highest C-RCE (around 56%) concentrated in a few key partners such as Central and Northern Australia (CAU, NAU) and tropical to southern South America. This indicates that while ECA is highly exposed, its concurrent exposure mainly occurs through specific teleconnections with a few key partner regions rather than through connections with many regions worldwide. In contrast, MED functions as a global connectivity hub, maintaining high concurrency with a broad set of regions across multiple continents—including North America, Australia/New Zealand, Southern South America, and large parts of Africa. This extensive linkage amplifies MED’s already elevated regional GDP exposure to heatwaves.

In summary, regional disparities in GDP affected by heatwaves are already pronounced in the mid-century and can be further amplified by elevated simultaneous exposure to heatwaves across multiple regions. For regions with high regional exposure, cross-regional exposure acts as a multiplier, linking local economic shocks to global systemic risks. Some regions show concentrated connectivity with a few key partners, whereas others, like MED, are highly concurrent with many regions across continents, highlighting the spatial complexity of economic exposure to heat extremes.

GDP Affected by Spatially Compounding Heavy Precipitation

In the mid-century period, global GDP exposed to heavy precipitation events (GCE; Fig. 4a) is projected to reach 32 trillion USD under SSP5–8.5 (13–57 trillion USD; 5th–95th percentile), equivalent to about 6.4% of global GDP. Under SSP3–7.0, exposed GDP amounts to 13 trillion USD (4–24 trillion USD; 4.5% of GDP), while SSP2–4.5 yields an intermediate estimate of ≈ 16 trillion USD (6–30 trillion USD; ≈ 4.5% of GDP). In all scenarios, climate effects drive the majority of exposure, although socio-economic factors contribute up to 0.5–1% of total exposure, reflecting GDP growth concentrated in regions particularly prone to heavy precipitation.

Significant regional disparities emerge (RCE; Fig. 4b), with tropical regions projected to experience the largest increases: in the SSP2–4.5 mid-century scenario, relative exposure reaches about three times the global average. In several tropical regions—particularly Northern South America (NSA), Central Africa (CAF), South Asia (SAS), and SAM—economic growth in precipitation-prone areas further amplifies exposure. Socio-economic effects dominate in NSA and contribute up to one-fifth in other regions, while climate effects remain the primary driver overall (Supplementary Figure 3).

Notably, these regions also exhibit high concurrent exposure to global events (L-GCE; Fig. 4c), potentially amplifying economic impacts through spatially compounding effects. The highest L-GCE arises when the spatial compounding effect of tropical African regions is considered together with concurrent impacts across the rest of the world. Under the SSP2–4.5 mid-century scenario, a heavy precipitation event in these regions would result in approximately 18 trillion USD being concurrently affected on a global scale. A substantial share of these elevated local effects is driven by intra-continental C-RCE (Fig. 4d) and its overlaps with tropical South American regions—Northeastern South America (NES), Northwestern South America (NWS), SAM, and NSA- and, to a lesser extent, with selected high-latitude, boreal, and Australasian regions.

In summary, regional disparities in GDP affected by heavy precipitation are projected to widen markedly, with the strongest increases in relative exposure occurring in the tropics, where extreme rainfall events become more frequent. These physical changes coincide with rapid socio-economic growth, amplifying regional exposure such that relative GDP affected reaches up to three times the global mean. At the same time, intra-continental simultaneous exposure—linking multiple African regions experiencing heavy precipitation concurrently—further heightens economic risk, underscoring the growing importance of intra-continental compounding effects.

GDP Affected by Spatially Compounding Soil Moisture Droughts

Under SSP5–8.5, global concurrent GDP affected by soil moisture droughts (GCE; Fig. 5a) is projected to reach 37 trillion USD in the mid-century period (10–75 trillion USD; 5th–95th percentile), corresponding to roughly 6.5% of global GDP (–2 to 18%), with all near-term exposure driven by climatic effects. As for heavy precipitation, SSP5 exhibits the highest absolute and relative exposure due to the strong physical intensification of extreme events. Under SSP3–7.0, the exposure is lower, amounting to 16 trillion USD (3–30 trillion USD) in the mid-century period, with relative exposure of 5.2% (–2 to 15%). SSP2–4.5 falls between these extremes, with absolute exposure of ≈ 20 trillion USD (5–43 trillion USD) and relative exposure of ≈ 5% (–2 to 15%), to which socio-economic effects contribute approximately 0.3%.

Under the SSP2–4.5 mid-century scenario, regional disparities in GDP affected by soil moisture drought intensify (RCE; Fig. 5b), with drought-prone regions reaching up to three times the global average in relative exposure and largely coinciding with areas projected to experience pronounced drying8, including NSA, NES, SAM, and South-Western South America (SWS) in the Americas; ESAF, WSAF, and Madagascar (MDG) in Africa; and Western and Central Europe (WCE) and MED in Europe and the Mediterranean. Although southern North America is also projected to experience drying, it does not emerge as a regional hotspot for high RCE in our analysis. Whereas for heavy precipitation events, high RCE strongly coincides with regions experiencing rapid GDP growth—enhancing relative GDP exposure, but this socio-economic amplification applies only to some drought hotspots. For example, NES and SAM both exhibit similarly high relative GDP exposure (RCE) to soil moisture droughts (≈ 13%), but the contributions of climate and socio-economic effects differ. NES shows moderate GDP growth (Fig. 1c), yet because it occurs in drought-prone areas, relative exposure is amplified (Supplementary Figure 3). In contrast, SAM is projected to undergo higher GDP growth, but much of it is outside drought-prone regions, dampening socio-economic amplification.

The amplification of potential economic impacts through global compounding effects (L-GCE; Fig. 5c) remains pronounced in these regions. Under the SSP2–4.5 mid-century scenario, up to 27 trillion USD are projected to be affected concurrently on a global scale. Regions highly impacted by soil moisture droughts also exhibit substantial simultaneous exposure with other drying regions (C-RCE; Fig. 5d). For example, South American and African drying regions display exceptionally high inter-continental concurrent exposure, whereas Southern Australia (SAU) and New Zealand (NZ) show strong concurrent exposure with the South American and European drying regions.

In summary, GDP affected by soil moisture droughts exhibits pronounced regional disparities, with drought-prone areas in South America, Africa, and the Mediterranean experiencing up to three times the global average relative exposure. While the physical intensification of droughts drives most of the GDP exposure, concentrated GDP growth in select regions further amplifies local economic exposure. These regions also show substantial simultaneous exposure with other drying areas, both within and across continents, highlighting the compounding nature of drought impacts and the potential for amplified global economic consequences.

GDP Affected by Spatially Compounding Water Scarcity

In the mid-century period under SSP5–8.5, annual global GDP exposed to water scarcity (GCE; Fig. 6a) is projected to reach 30 trillion USD (9–61 trillion USD; 5th–95th percentile), corresponding to roughly 4.8% of global GDP (–2 to 15%). Under SSP3–7.0, absolute exposure is lower, amounting to 11 trillion USD (2.5–25 trillion USD; 3% of GDP, –3 to 11%). SSP2–4.5 shows intermediate values, with absolute exposure of ≈ 17 trillion USD (4.4–34 trillion USD; 3.7% of GDP, –3 to 12%) and a comparatively smaller contribution from socio-economic growth.

In all scenarios, climate effects drive the majority of exposure, although socio-economic factors contribute between 1.5% (SSP2-4.5) and 8% (SP4-6.0), reflecting GDP growth concentrated in regions particularly prone to water scarcity.

Under the SSP2–4.5 mid-century scenario, regions with high relative GDP exposure to water scarcity (RCE; Figure 6b) only partly overlap with areas experiencing pronounced drying8,17. Notable hotspots include NSA, SAM, SAU, and MED, with RCE around 6–7% of regional GDP (3–10%), roughly twice the global mean. Other regions, such as the Sahara (SAH), RAR, and ECA, also show elevated exposure. Across most regions, climate effects dominate, while socio-economic growth amplifies exposure in some areas, particularly parts of SAH and RAR. In contrast, in NSA, GDP growth occurs mostly outside the most water-stressed areas, slightly moderating the overall exposure signal (Supplementary Figure 3).

Regions such as Western and Central Africa (WAF, CAF) and SAH exhibit the most pronounced global spatial compounding effects (L-GCE; Fig. 6c), with up to 10.7 trillion USD (0–34 trillion USD) in economic value concurrently affected globally in the mid-century period.

SAH, WAF, CAF show strong links with regions across Europe, Australasia, and tropical South America (C-RCE; Fig. 6d), highlighting their key role in cross-regional concurrent exposure, while other regions experience elevated but comparatively lower impacts.

In summary, GDP affected by water scarcity exhibits pronounced regional disparities, with some regions experiencing up to twice the global average relative exposure. Climate effects dominate GDP exposure in all regions, while socio-economic growth further amplifies exposure in select areas—particularly in parts of the Sahara and the Russian Arctic—where economic development occurs in already water-stressed regions. In contrast, in NSA, GDP growth is projected to concentrate outside the most water-stressed areas, slightly dampening local exposure. WAF, CAF, and SAH emerge as the primary hotspots for simultaneous global exposure, exhibiting pronounced cross-regional effects across Europe, Australasia, and tropical South America. These patterns highlight the potential for compounding economic impacts of water scarcity and the combined importance of climatic and socio-economic drivers in shaping global and regional disparities.

Methods
Climate extreme indicators

To assess future changes in the global GDP affected by spatially compounding heatwaves, heavy precipitation, soil moisture droughts, and water scarcity, Earth System Models (ESMs) contributing to the Scenario Model Intercomparison Project44,45 of the Coupled Model Intercomparison Project phase 6 (CMIP6) are used. We consider the first five available ensemble members of historical (1850–2014)44 and future simulations (2015–2100) under five socioeconomic pathways paired with corresponding emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, SSP5-8.5)14–16, giving equal weight to each model. Detailed information on the ESMs, including model name and ensemble members, is summarized for reference in Supplementary Tables 1–4. The CMIP6 data undergo centralized preprocessing, which involves interpolating model outputs to a common 2.5° grid using second-order conservative remapping, resolving dimensional and structural inconsistencies, and excluding files with highly unrealistic values. We analyze daily maximum temperature, total daily precipitation, monthly soil moisture within the root zone (top 10 cm), and monthly precipitation-evapotranspiration (P-E) from all models and simulation runs to determine the extreme event indicators.

The selected extreme event indicators—heatwaves, heavy precipitation, soil moisture drought, and water scarcity—follow established definitions assessed in the IPCC Sixth Assessment Report8 and were chosen because they represent well-understood manifestations of anthropogenic climate change, capturing events of high societal relevance across key sectors including health, agriculture, and infrastructure.

The indicators are characterized using a percentile-based event definition. The percentile climatology of each event type is calculated based on the 95th percentile calculated from the reference period 1850–1900. The percentile climatology as well as the occurrence of the event type is calculated for each grid cell of the simulated data.

The heatwave indicator is defined based on a 14-day moving average of daily maximum temperature and its subsequently extracted annual maximum. Each extracted annual 14-day temperature maximum that exceeds the 95th percentile climatology is defined as a year in which a heatwave occurs.

Heavy precipitation events are defined based on a five-day moving precipitation sum and its subsequently extracted annual maximum. Each extracted annual five-day precipitation sum that exceeds the amount of precipitation in the 95th percentile climatology is defined as a year in which a heavy precipitation event occurs.

Soil moisture drought events are defined based on monthly root-zone soil moisture sums and the corresponding annual minima. Any annual minimum that falls below the 5th percentile of the baseline climatology is considered a year in which a soil moisture drought occurs.

Years with the occurrence of water scarcity are defined based on the monthly precipitation-evapotranspiration (P-E) sums and the corresponding annual minima. Any annual that falls below the 5th percentile of the baseline climatology is defined as a water-scarce year.

Quantification of GDP affected by climate extremes

On a grid cell level, we intersect the extracted climate extreme events with global gridded GDP datasets consistent with the Shared Socioeconomic Pathways (SSPs)33. The GDP datasets (in 2005 PPP USD) provide annual values for the historical period 2000–2020 and projections from 2025 to 2100 at 5-year intervals under the five SSPs. For intermediate years, values are assigned to the nearest 5-year timestep (e.g., 2026–2029 are assigned to 2025). The exposure dataset is aggregated to a 2.5° × 2.5° grid using an area-weighted sum of the high-resolution cells to facilitate comparison with ESM simulations while preserving absolute GDP values.

GDP affected is quantified at multiple scales using the following metrics: Global Concurrent GDP affected (GCE), Regional Concurrent GDP affected (RCE, by AR6 regions46, Supplementary Figure 1), Local effects of Globally Concurrent GDP affected (L-GCE), and Cross-Regional GDP affected (C-RCE).

The fraction of GDP affected is calculated as shown in equation (1).

Changes in GDP affected are expressed relative to the mean of the present-day period (2001–2020) and computed according to equation (2):

where X present-day denotes the average over the baseline years. All subsequent changes are reported relative to this baseline, highlighting additional future effects compared to present-day levels.

To attribute changes in GDP affected, we quantify the fraction of GDP exposed to climate extremes and decompose its future changes into two main contributions: (i) climate-driven, reflecting how exposure evolves due to changing climate extremes while holding GDP constant at 2020 levels, and (ii) socio-economic-driven, capturing changes due to GDP growth, including regions where economic development coincides with stronger climate exposure47.

For this, we define the following components, illustrated here for Global Concurrent GDP affected (GCE). GCE(t) represents the full model, where both climate and GDP evolve simultaneously. GCE fixedGDP(t) captures the climate effect, representing changes in GCE due to evolving climate extremes while holding GDP constant at 2020 levels. GCE fixedClimate(t) captures the socio-economic effect, representing changes in GCE due to GDP growth while keeping climate extremes at 1850–1900 levels. Finally, GCE interaction(t) is the interaction term, which captures additional exposure in regions where economic growth coincides with more frequent climate extremes; this term cannot be directly attributed to climate or socio-economic effects alone but emerges from their joint evolution.

The total change in GCE can be decomposed as described in equation (3):

where the interaction term is computed according to equation (4):

For reporting, the socio-economic and interaction contributions are typically combined to summarize the total effect of economic development. Multi-model means summarize central tendencies across climate models.

Data Availability. All original CMIP6 data14,44,45 used in this study are publicly available https://esgf-node.ipsl.upmc.fr/search/cmip6-ipsl/. The models and ensemble members used within this study are listed in Supplementary Tables 1-4. The Global gridded GDP under the historical and future scenarios48 is publicly available https://zenodo.org/records/7898409.

Code Availability. All code necessary to reproduce the analysis is made available on and permanently stored at https://doi.org/10.5281/zenodo.17751353.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript explores the connection between the increase in spatially compounded extreme events of heat, wet, and dry and global economic risks, analyzing how the cross-regional and planetary-scale dependencies of extreme climate exacerbate regional disparities in economic exposure. This perspective and methodology hold meaningful reference and meaning for socioeconomic decision-making.\nHowever, the research is based on static nominal GDP exposure from 2020, and future GDP may differ significantly from that of 2020. The author should provide evidence to clarify the rationality behind this approach. In addition, the authors must address the following issues:\nMajor Comments:\n1.As the authors states, future GDP exposure to climate extremes will also be modulated by changes in GDP levels associated with future societal development. In fact, changes in future GDP exposure may be influenced by three aspects: climate effects, GDP effects, and climate-GDP interaction effects. The author considers the static nominal GDP of 2020 and examines the impact of future compound extreme risks on GDP exposure. However, this premise must be that future GDP exposure is mainly affected by climate effects. Otherwise, the relevance of the results for social decision-making may be significantly diminished, potentially leading to misjudgments and greater economic losses. The author should clarify this crucial issue.\n2.P5 Fig.1: As for Cross-Regional Concurrent GDP Affected, it is hard to understand the criteria for the selection and arrangement of the regions corresponding to the X-axis and Y-axis. Why are there 22 regions on the Y-axis and 21 regions on the X-axis? Does this seem random? What does the value with add. Fraction of GDP represent when there is no corresponding region on the X-axis and Y-axis? It is easy to understand the methodology, but difficult to understand the results of Cross-Regional Concurrent GDP Affected.\n3.In the study, the connections between economic exposure and four extreme events were respectively explored, but there was a lack of comparative discussions on the exposure of different extreme events to GDP. For example, among the four extreme events mentioned, which event might have a greater impact on the economic exposure of low-income countries? Because these four events are interrelated, for instance, heatwaves can cause water scarcity. Therefore, results about water scarcity might be part of the consequences caused by heatwaves.\nMinor Comments:\n1.P3 L76: For 43 trillion USD of GDP, what is the range of its uncertainty? Similarly, GDP related to other extreme events should also be provided.\n2.P3 L90: The Arctic amplification can also have an impact on heatwaves.\n3.P11 L277: Why is it a season? Maybe it's a year according to your description?\n4.P11 L281, L284: For events that occur once every 20 years, it undershoots 5%. This description is ambiguous and may lead readers to mistakenly believe that it represents a range from the 95th to the 100th.\n5.P11 L282: Is the root zone the surface soil layer (0-10 cm)? Please provide references.\n6.P11 4.2GDP affected: Why is the reference time for nightlight intensity data 2016 and that for population data 2020? Are the populations and the nightlight intensity of all grids simultaneously used as weights for GDP exposure? Is there any situation where the nightlight intensity or population data of the grids are missing? If so, how to assign weights?\n7.P11 L305: According to what weighting? The author should elaborate on the methods used.\n8.Appendix A: There are overlaps between the colorbar and the results. Please modify it.", "reviewer_2": "This manuscript explores the connection between the increase in spatially compounded extreme events of heat, wet, and dry and global economic risks, analyzing how the cross-regional and planetary-scale dependencies of extreme climate exacerbate regional disparities in economic exposure. This perspective and methodology hold meaningful reference and meaning for socioeconomic decision-making.\nHowever, the research is based on static nominal GDP exposure from 2020, and future GDP may differ significantly from that of 2020. The author should provide evidence to clarify the rationality behind this approach. In addition, the authors must address the following issues:\nMajor Comments:\n1.As the authors states, future GDP exposure to climate extremes will also be modulated by changes in GDP levels associated with future societal development. In fact, changes in future GDP exposure may be influenced by three aspects: climate effects, GDP effects, and climate-GDP interaction effects. The author considers the static nominal GDP of 2020 and examines the impact of future compound extreme risks on GDP exposure. However, this premise must be that future GDP exposure is mainly affected by climate effects. Otherwise, the relevance of the results for social decision-making may be significantly diminished, potentially leading to misjudgments and greater economic losses. The author should clarify this crucial issue.\n2.P5 Fig.1: As for Cross-Regional Concurrent GDP Affected, it is hard to understand the criteria for the selection and arrangement of the regions corresponding to the X-axis and Y-axis. Why are there 22 regions on the Y-axis and 21 regions on the X-axis? Does this seem random? What does the value with add. Fraction of GDP represent when there is no corresponding region on the X-axis and Y-axis? It is easy to understand the methodology, but difficult to understand the results of Cross-Regional Concurrent GDP Affected.\n3.In the study, the connections between economic exposure and four extreme events were respectively explored, but there was a lack of comparative discussions on the exposure of different extreme events to GDP. For example, among the four extreme events mentioned, which event might have a greater impact on the economic exposure of low-income countries? Because these four events are interrelated, for instance, heatwaves can cause water scarcity. Therefore, results about water scarcity might be part of the consequences caused by heatwaves.\nMinor Comments:\n1.P3 L76: For 43 trillion USD of GDP, what is the range of its uncertainty? Similarly, GDP related to other extreme events should also be provided.\n2.P3 L90: The Arctic amplification can also have an impact on heatwaves.\n3.P11 L277: Why is it a season? Maybe it's a year according to your description?\n4.P11 L281, L284: For events that occur once every 20 years, it undershoots 5%. This description is ambiguous and may lead readers to mistakenly believe that it represents a range from the 95th to the 100th.\n5.P11 L282: Is the root zone the surface soil layer (0-10 cm)? Please provide references.\n6.P11 4.2GDP affected: Why is the reference time for nightlight intensity data 2016 and that for population data 2020? Are the populations and the nightlight intensity of all grids simultaneously used as weights for GDP exposure? Is there any situation where the nightlight intensity or population data of the grids are missing? If so, how to assign weights?\n7.P11 L305: According to what weighting? The author should elaborate on the methods used.\n8.Appendix A: There are overlaps between the colorbar and the results. Please modify it.", "reviewer_3": "I've reviewed the manuscript titled \"Global Economic Exposure to Climate Change Amplified by Spatially Compounding Climate Extremes.\" Here are my suggestions and recommendations:\n1. Grammar and Corrections\nThe manuscript is generally well-written, but there are a few areas where grammar and phrasing could be improved:\n• Abstract: \"Here, we demonstrate a crucial link between the projected increase in spatially compounding hot, wet, and dry extremes and the amplification of global economic exposure.\" Consider rephrasing for clarity: \"We demonstrate a crucial link between the projected increase in spatially compounding hot, wet, and dry extremes and the amplification of global economic exposure.\"\n• Introduction: \"Global economic losses due to weather-related events have increased eight-fold from the 1970s to the 2010s reaching a cumulative economic loss of 1.48 trillion USD within the period 2010 - 2019.\" Suggest adding a comma for readability: \"Global economic losses due to weather-related events have increased eight-fold from the 1970s to the 2010s, reaching a cumulative economic loss of 1.48 trillion USD within the period 2010 - 2019.\"\n• Results: \"The future additional global annual GDP affected by Global Concurrent Extremes (GCE) — defined as extreme events, such as heatwaves, that occurred on average every 20 years in a preindustrial climate — is projected to increase significantly under enhanced global warming compared to present-day conditions (+1.2°C of global warming).\" This sentence is quite long and could be broken into two for better readability.\n2. Methodology\nThe methodology is comprehensive and well-detailed. However, it could benefit from a clearer structure:\n• Climate Extreme Indicators: The definitions of heatwaves, heavy precipitation, water scarcity, and agroecological droughts are clear. Consider adding a brief explanation of why these specific indicators were chosen.\n• GDP Affected: The process of intersecting climate extreme events with the GDP exposure dataset is well-explained. It might be helpful to include a flowchart or diagram to visually represent this process.\n• Calculation of Global Warming Levels: This section is detailed but could be simplified. Consider summarizing the key steps and providing references for readers who want more in-depth information.\n3. Data Analysis\nThe data analysis is thorough and uses appropriate statistical methods. Here are some suggestions:\n• Figures and Tables: The figures are informative but could be accompanied by more detailed captions explaining the key findings. For example, Figure 1 could include a brief summary of the main trends observed.\n• Regional Disparities: The analysis of regional disparities is insightful. Consider adding more context about why certain regions are more affected and how this aligns with existing literature.\n4. Clarity of Results\nThe results are generally clear, but some sections could be enhanced for better understanding:\n• Heatwaves: The discussion on heatwaves is detailed. Consider summarizing the key points at the end of the section to reinforce the main findings.\n• Heavy Precipitation, Soil Moisture Drought, and Water Scarcity: These sections are well-explained. Adding a summary table comparing the impacts of these different extremes on GDP could be helpful.\n5. Conclusion\nThe conclusion effectively summarizes the study's findings but could be more impactful:\n• Key Takeaways: Highlight the most significant findings and their implications for policy and future research.\n• Recommendations: Provide specific recommendations for policymakers and researchers based on the study's results."}, "author_response": "We thank the reviewer for the thoughtful comments regarding the implications of using static nominal GDP versus time-variant GDP levels. We fully acknowledge the importance of incorporating future GDP trajectories rather than relying solely on static 2020 values. In response, we have substantially revised our analysis and now assess the evolution of GDP exposed to spatially compounding climate extremes across five distinct future GDP scenarios.\nTo maintain a reasonable manuscript length, we present the full scenario trajectories in the global analysis, while the main regional assessment focuses on mid-term (2041–2060) exposure levels under SSP2-4.5, relative to present-day (2001–2020).\nResults for additional time horizons and alternative socioeconomic pathways are provided in the Extended Data section\nMajor Comments:\n1.As the authors states, future GDP exposure to climate extremes will also be modulated by changes in GDP levels associated with future societal development. In fact, changes in future GDP exposure may be influenced by three aspects: climate effects, GDP effects, and climate-GDP interaction effects. The author considers the static nominal GDP of 2020 and examines the impact of future compound extreme risks on GDP exposure. However, this premise must be that future GDP exposure is mainly affected by climate effects. Otherwise, the relevance of the results for social decision-making may be significantly diminished, potentially leading to misjudgments and greater economic losses. The author should clarify this crucial issue.\nWe thank the reviewer for the detailed feedback regarding the different drivers. To address this, we now incorporate projected GDP trajectories and compute the fraction of GDP exposed under time-varying GDP conditions (revised Figure 2b/4a/5a/6a). For comparison, we also perform calculations using static nominal GDP from 2020 to isolate the impact of climate change alone.\nAs expected, the absolute amount of GDP exposed is substantially higher when accounting for projected GDP growth, reflecting GDP increases under all SSP–RCP scenarios (see Figure 1b). However, the relative fraction of GDP exposed is only marginally affected by GDP growth. This is illustrated in the bar plots of revised Figures 2b/4a/5a/6, which explicitly separate the contributions from changes in extreme event occurrence (“climate effects”) and from GDP growth (“socio-economic effects”, encompassing both GDP growth effects and climate–GDP interaction effects). Across all climate extremes investigated, the dominant driver of changes in relative global economic exposure is climate-induced variation in extreme event occurrence, although the magnitude of this effect varies by event type and region. Extended Figure A3 provides the same decomposition at the regional level.\n2.P5 Fig.1: As for Cross-Regional Concurrent GDP Affected, it is hard to understand the criteria for the selection and arrangement of the regions corresponding to the X-axis and Y-axis. Why are there 22 regions on the Y-axis and 21 regions on the X-axis? Does this seem random? What does the value with add. Fraction of GDP represent when there is no corresponding region on the X-axis and Y-axis? It is easy to understand the methodology, but difficult to understand the results of Cross-Regional Concurrent GDP Affected.\nWe thank the reviewer for pointing this out. In the original version, we labeled only every second region on the x-axis (and the corresponding opposite on the y-axis) to maintain a readable font size. We agree that this choice may have caused confusion regarding the selection and arrangement of regions.\nTo address this, we have revised Figures 3–6 (and Extended Figures A4-A29) in the updated manuscript so that all regions are now displayed on both axes. This improves clarity and allows for a more intuitive interpretation of the Cross-Regional Concurrent GDP Affected results.\n3.In the study, the connections between economic exposure and four extreme events were respectively explored, but there was a lack of comparative discussions on the exposure of different extreme events to GDP. For example, among the four extreme events mentioned, which event might have a greater impact on the economic exposure of low-income countries? Because these four events are interrelated, for instance, heatwaves can cause water scarcity. Therefore, results about water scarcity might be part of the consequences caused by heatwaves.\nWe thank the reviewer for this thoughtful and constructive comment. In response, we have expanded the discussion section (Lines 282–368 in the updated manuscript) to provide a broader reflection on the historical economic impacts of observed extreme events, highlighting which regions have been disproportionately affected and through which impact channels. We also compare these insights with our key findings on projected regional disparities and their amplification through the global and regional co-occurrence of events.\nWe refrain from drawing definitive conclusions about which extreme event type might have the greatest economic impact in specific regions or income groups, as our analysis is based on exposure assessments rather than impact assessments. Establishing causal economic damages would require explicit modeling of vulnerability and adaptive capacity, which is beyond the scope of this study. Our approach is therefore designed to describe changes in exposure without inferring impacts that cannot be directly derived from our results. To avoid potential misunderstandings, we have clarified more explicitly throughout the manuscript that our analysis focuses on exposure extreme events rather than GDP impacts of extreme events. For example, we revised the title of Section 2.1 in the updated manuscript from “Impacts” to “Exposure” to better reflect this distinction.\nWe also now emphasize that although the four extreme event types can be correlated (e.g., heat extremes contributing to water scarcity), we assess their effects on economic exposure individually. To prevent misinterpretation, we explicitly state in the updated manuscript (L297-303) that we investigate univariate extremes and that exposure estimates for different event types should not be interpreted as additive. Finally, to account for potential overlaps in economic exposure to different extreme events, we have added Figure A4 to the Extended Figures, which shows the exposure of GDP to the occurrence of at least one extreme event type (i.e., heatwave and/or heavy precipitation and/or water scarcity and/or soil moisture drought).\nMinor Comments:\n1.P3 L76: For 43 trillion USD of GDP, what is the range of its uncertainty? Similarly, GDP related to other extreme events should also be provided.\nWe added the 5th–95th percentile uncertainty ranges to all reported values.\n2.P3 L90: The Arctic amplification can also have an impact on heatwaves.\nWe thank the reviewer for pointing this out. Our initial statement was intended to convey that, despite the phenomenon of Arctic amplification, tropical regions exhibit particularly enhanced exposure, even though one might expect the high-latitude regions to show the highest exposure. We did not mean to imply that Arctic amplification has no effect on tropical exposure, but we understand how this could have been misinterpreted. We have therefore decided to remove this sentence for clarity.\n3.P11 L277: Why is it a season? Maybe it's a year according to your description?\nWe thank the reviewer for spotting this detail. We apologize for the confusion, as we intended to refer to a year in which a heavy precipitation event occurs, not a season associated with a wet spell. We have revised the manuscript accordingly.\n4.P11 L281, L284: For events that occur once every 20 years, it undershoots 5%. This description is ambiguous and may lead readers to mistakenly believe that it represents a range from the 95th to the 100th.\nWe thank the reviewer for pointing this out. We agree that the original phrasing was ambiguous. To clarify, we calculated the undershoot of the 5th percentile of annual soil moisture drought (MRSOS) or P–E (for water scarcity) monthly minima. We have revised the text accordingly to avoid any potential misinterpretation.\n5.P11 L282: Is the root zone the surface soil layer (0-10 cm)? Please provide references.\nYes, the root zone of the surface layer refers to the top 10 cm of the soil. We have added this clarification to the manuscript.\n6.P11 4.2GDP affected: Why is the reference time for nightlight intensity data 2016 and that for population data 2020? Are the populations and the nightlight intensity of all grids simultaneously used as weights for GDP exposure? Is there any situation where the nightlight intensity or population data of the grids are missing? If so, how to assign weights?\nWe thank the reviewer for raising this point. As we no longer rely on the global high-resolution GDP exposure dataset provided by Eberenz et al. (2020), this comment is no longer relevant. In the revised manuscript, we now refer to the global gridded GDP dataset consistent with the Shared Socioeconomic Pathways (SSPs).\n7.P11 L305: According to what weighting? The author should elaborate on the methods used.\nWe thank the reviewer for pointing out the missing description. Due to our revised calculation approach, we no longer rely on global mean temperatures. Therefore, we consider this comment no longer relevant.\n8.Appendix A: There are overlaps between the colorbar and the results. Please modify it.\nWe thank the reviewer for pointing out this issue. Based on our updated calculations, we replotted the data and ensured that no overlaps remain.\nReviewer #2 (Remarks to the Author):\nI've reviewed the manuscript titled \"Global Economic Exposure to Climate Change Amplified by Spatially Compounding Climate Extremes.\" Here are my suggestions and recommendations:\n1.Grammar and Corrections\nThe manuscript is generally well-written, but there are a few areas where grammar and phrasing could be improved:\n• Abstract: \"Here, we demonstrate a crucial link between the projected increase in spatially compounding hot, wet, and dry extremes and the amplification of global economic exposure.\" Consider rephrasing for clarity: \"We demonstrate a crucial link between the projected increase in spatially compounding hot, wet, and dry extremes and the amplification of global economic exposure.\"\nWe thank the reviewer for the suggestion. We have updated the sentence accordingly.\n• Introduction: \"Global economic losses due to weather-related events have increased eight-fold from the 1970s to the 2010s reaching a cumulative economic loss of 1.48 trillion USD within the period 2010 - 2019.\" Suggest adding a comma for readability: \"Global economic losses due to weather-related events have increased eight-fold from the 1970s to the 2010s, reaching a cumulative economic loss of 1.48 trillion USD within the period 2010 - 2019.\"\nWe have made the corresponding change.\n• Results: \"The future additional global annual GDP affected by Global Concurrent Extremes (GCE) — defined as extreme events, such as heatwaves, that occurred on average every 20 years in a preindustrial climate — is projected to increase significantly under enhanced global warming compared to present-day conditions (+1.2°C of global warming).\" This sentence is quite long and could be broken into two for better readability.\nWe revised the sentence and broke it into two parts to enhance clarity.\n2. Methodology\nThe methodology is comprehensive and well-detailed. However, it could benefit from a clearer structure:\n• Climate Extreme Indicators: The definitions of heatwaves, heavy precipitation, water scarcity, and agroecological droughts are clear. Consider adding a brief explanation of why these specific indicators were chosen.\nWe thank the reviewer for this suggestion. The selected indicators (heatwaves, heavy precipitation, water scarcity, and agroecological drought) follow definitions used in the IPCC Sixth Assessment Report and were chosen because they represent well-understood manifestations of anthropogenic climate change with high societal relevance across key sectors, including health, agriculture, and infrastructure. We added this explanation to the manuscript (L445-L449).\n• GDP Affected: The process of intersecting climate extreme events with the GDP exposure dataset is well-explained. It might be helpful to include a flowchart or diagram to visually represent this process.\nWe thank the reviewer for this helpful suggestion. A flowchart has been added to the main text (Figure 1 in the revised manuscript) to visually illustrate how climate extreme events are intersected with the GDP exposure dataset, thereby clarifying the methodological workflow.\n• Calculation of Global Warming Levels: This section is detailed but could be simplified. Consider summarizing the key steps and providing references for readers who want more in-depth information.\nWe thank the reviewer for the suggestion. We have revised our analysis to compare the near-term (2041–2060) and far-term (2081–2100) periods with the present-day period (2001–2020), instead of describing results in terms of global warming levels. Consequently, the section on calculating global warming levels has been removed.\n3. Data Analysis\nThe data analysis is thorough and uses appropriate statistical methods. Here are some suggestions:\n• Figures and Tables: The figures are informative but could be accompanied by more detailed captions explaining the key findings. For example, Figure 1 could include a brief summary of the main trends observed.\nWe thank the reviewer for this valuable suggestion. Due to journal-imposed caption length limits, extended summaries cannot be added directly to figure captions. Instead, we have added a concise summary paragraph at the end of each variable section to reinforce the key trends and improve readability, as also recommended by the reviewer.\n• Regional Disparities: The analysis of regional disparities is insightful. Consider adding more context about why certain regions are more affected and how this aligns with existing literature.\nWe thank the reviewer for this helpful suggestion. We expanded the discussion of regional disparities in two ways:\no Results section: We investigate which driver (socio-economic effects or climate effects) primarily drives the additional GDP exposure and compare regional differences.\no Discussion section: For each extreme event type, we now provide a summary of existing literature on which regions are expected to be most affected due to human-induced climate change. We then compare this knowledge to our results by showing where projected increases in extreme event occurrence are reflected in our analysis of heightened GDP exposure. This explicitly connects our findings to the broader literature and contextualizes regional differences.\n4. Clarity of Results\nThe results are generally clear, but some sections could be enhanced for better understanding:\n• Heatwaves: The discussion on heatwaves is detailed. Consider summarizing the key points at the end of the section to reinforce the main findings.\nWe added a summary paragraph at the end of each extreme event section (including heatwaves) to highlight the key takeaways. We agree that this improves clarity and helps the reader to focus on the main findings.\n• Heavy Precipitation, Soil Moisture Drought, and Water Scarcity: These sections are well-explained. Adding a summary table comparing the impacts of these different extremes on GDP could be helpful.\nWe thank the reviewer for this suggestion. As noted above, we have added summary paragraphs for all extreme event types. To avoid overloading the main text, we have not included a summary table, as our study focuses primarily on the qualitative implications of spatially compounding climate extremes. However, should the reviewer consider the table essential, we would be pleased to include it in a revised version or as supplementary material.\n5. Conclusion\nThe conclusion effectively summarizes the study's findings but could be more impactful:\n• Key Takeaways: Highlight the most significant findings and their implications for policy and future research.\n• Recommendations: Provide specific recommendations for policymakers and researchers based on the study's results.\nWe thank the reviewer for this valuable suggestion. As noted above, we have summarized the key findings for each extreme event type within their respective sections. In addition, we have expanded the discussion and included a dedicated closing paragraph that synthesizes the main insights of the study (see Lines 404–418 in the revised manuscript). While our work aims to provide policy-relevant evidence, we intentionally refrain from making prescriptive recommendations for policymakers. The primary objective of this study is to deliver robust scientific evidence that can inform decision-making without imposing specific policy directives.\nDecision: Major Revision\nGiven the thoroughness of the study and the relatively minor issues identified, I would recommend a Major revision.\nWe appreciate the reviewer’s careful assessment and constructive feedback, which have substantially improved the clarity and quality of our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This manuscript explores the connection between the increase in spatially compounded extreme events of heat, wet, and dry and global economic risks, analyzing how the cross-regional and planetary-scale dependencies of extreme climate exacerbate regional disparities in economic exposure. This perspective and methodology hold meaningful reference and meaning for socioeconomic decision-making.\nHowever, the research is based on static nominal GDP exposure from 2020, and future GDP may differ significantly from that of 2020. The author should provide evidence to clarify the rationality behind this approach. In addition, the authors must address the following issues:\nWe thank the reviewer for the thoughtful comments regarding the implications of using static nominal GDP versus time-variant GDP levels. We fully acknowledge the importance of incorporating future GDP trajectories rather than relying solely on static 2020 values. In response, we have substantially revised our analysis and now assess the evolution of GDP exposed to spatially compounding climate extremes across five distinct future GDP scenarios.\nTo maintain a reasonable manuscript length, we present the full scenario trajectories in the global analysis, while the main regional assessment focuses on mid-term (2041–2060) exposure levels under SSP2-4.5, relative to present-day (2001–2020).\nResults for additional time horizons and alternative socioeconomic pathways are provided in the Extended Data section\nMajor Comments:\n1.As the authors states, future GDP exposure to climate extremes will also be modulated by changes in GDP levels associated with future societal development. In fact, changes in future GDP exposure may be influenced by three aspects: climate effects, GDP effects, and climate-GDP interaction effects. The author considers the static nominal GDP of 2020 and examines the impact of future compound extreme risks on GDP exposure. However, this premise must be that future GDP exposure is mainly affected by climate effects. Otherwise, the relevance of the results for social decision-making may be significantly diminished, potentially leading to misjudgments and greater economic losses. The author should clarify this crucial issue.\nWe thank the reviewer for the detailed feedback regarding the different drivers. To address this, we now incorporate projected GDP trajectories and compute the fraction of GDP exposed under time-varying GDP conditions (revised Figure 2b/4a/5a/6a). For comparison, we also perform calculations using static nominal GDP from 2020 to isolate the impact of climate change alone.\nAs expected, the absolute amount of GDP exposed is substantially higher when accounting for projected GDP growth, reflecting GDP increases under all SSP–RCP scenarios (see Figure 1b). However, the relative fraction of GDP exposed is only marginally affected by GDP growth. This is illustrated in the bar plots of revised Figures 2b/4a/5a/6, which explicitly separate the contributions from changes in extreme event occurrence (“climate effects”) and from GDP growth (“socio-economic effects”, encompassing both GDP growth effects and climate–GDP interaction effects). Across all climate extremes investigated, the dominant driver of changes in relative global economic exposure is climate-induced variation in extreme event occurrence, although the magnitude of this effect varies by event type and region. Extended Figure A3 provides the same decomposition at the regional level.\n2.P5 Fig.1: As for Cross-Regional Concurrent GDP Affected, it is hard to understand the criteria for the selection and arrangement of the regions corresponding to the X-axis and Y-axis. Why are there 22 regions on the Y-axis and 21 regions on the X-axis? Does this seem random? What does the value with add. Fraction of GDP represent when there is no corresponding region on the X-axis and Y-axis? It is easy to understand the methodology, but difficult to understand the results of Cross-Regional Concurrent GDP Affected.\nWe thank the reviewer for pointing this out. In the original version, we labeled only every second region on the x-axis (and the corresponding opposite on the y-axis) to maintain a readable font size. We agree that this choice may have caused confusion regarding the selection and arrangement of regions.\nTo address this, we have revised Figures 3–6 (and Extended Figures A4-A29) in the updated manuscript so that all regions are now displayed on both axes. This improves clarity and allows for a more intuitive interpretation of the Cross-Regional Concurrent GDP Affected results.\n3.In the study, the connections between economic exposure and four extreme events were respectively explored, but there was a lack of comparative discussions on the exposure of different extreme events to GDP. For example, among the four extreme events mentioned, which event might have a greater impact on the economic exposure of low-income countries? Because these four events are interrelated, for instance, heatwaves can cause water scarcity. Therefore, results about water scarcity might be part of the consequences caused by heatwaves.\nWe thank the reviewer for this thoughtful and constructive comment. In response, we have expanded the discussion section (Lines 282–368 in the updated manuscript) to provide a broader reflection on the historical economic impacts of observed extreme events, highlighting which regions have been disproportionately affected and through which impact channels. We also compare these insights with our key findings on projected regional disparities and their amplification through the global and regional co-occurrence of events.\nWe refrain from drawing definitive conclusions about which extreme event type might have the greatest economic impact in specific regions or income groups, as our analysis is based on exposure assessments rather than impact assessments. Establishing causal economic damages would require explicit modeling of vulnerability and adaptive capacity, which is beyond the scope of this study. Our approach is therefore designed to describe changes in exposure without inferring impacts that cannot be directly derived from our results. To avoid potential misunderstandings, we have clarified more explicitly throughout the manuscript that our analysis focuses on exposure extreme events rather than GDP impacts of extreme events. For example, we revised the title of Section 2.1 in the updated manuscript from “Impacts” to “Exposure” to better reflect this distinction.\nWe also now emphasize that although the four extreme event types can be correlated (e.g., heat extremes contributing to water scarcity), we assess their effects on economic exposure individually. To prevent misinterpretation, we explicitly state in the updated manuscript (L297-303) that we investigate univariate extremes and that exposure estimates for different event types should not be interpreted as additive. Finally, to account for potential overlaps in economic exposure to different extreme events, we have added Figure A4 to the Extended Figures, which shows the exposure of GDP to the occurrence of at least one extreme event type (i.e., heatwave and/or heavy precipitation and/or water scarcity and/or soil moisture drought).\nPage 2 / 6\nMinor Comments:\n1.P3 L76: For 43 trillion USD of GDP, what is the range of its uncertainty? Similarly, GDP related to other extreme events should also be provided.\nWe added the 5th–95th percentile uncertainty ranges to all reported values.\n2.P3 L90: The Arctic amplification can also have an impact on heatwaves.\nWe thank the reviewer for pointing this out. Our initial statement was intended to convey that, despite the phenomenon of Arctic amplification, tropical regions exhibit particularly enhanced exposure, even though one might expect the high-latitude regions to show the highest exposure. We did not mean to imply that Arctic amplification has no effect on tropical exposure, but we understand how this could have been misinterpreted. We have therefore decided to remove this sentence for clarity.\n3.P11 L277: Why is it a season? Maybe it's a year according to your description?\nWe thank the reviewer for spotting this detail. We apologize for the confusion, as we intended to refer to a year in which a heavy precipitation event occurs, not a season associated with a wet spell. We have revised the manuscript accordingly.\n4.P11 L281, L284: For events that occur once every 20 years, it undershoots 5%. This description is ambiguous and may lead readers to mistakenly believe that it represents a range from the 95th to the 100th.\nWe thank the reviewer for pointing this out. We agree that the original phrasing was ambiguous. To clarify, we calculated the undershoot of the 5th percentile of annual soil moisture drought (MRSOS) or P–E (for water scarcity) monthly minima. We have revised the text accordingly to avoid any potential misinterpretation.\n5.P11 L282: Is the root zone the surface soil layer (0-10 cm)? Please provide references.\nYes, the root zone of the surface layer refers to the top 10 cm of the soil. We have added this clarification to the manuscript.\n6.P11 4.2GDP affected: Why is the reference time for nightlight intensity data 2016 and that for population data 2020? Are the populations and the nightlight intensity of all grids simultaneously used as weights for GDP exposure? Is there any situation where the nightlight intensity or population data of the grids are missing? If so, how to assign weights?\nWe thank the reviewer for raising this point. As we no longer rely on the global high-resolution GDP exposure dataset provided by Eberenz et al. (2020), this comment is no longer relevant. In the revised manuscript, we now refer to the global gridded GDP dataset consistent with the Shared Socioeconomic Pathways (SSPs).\n7.P11 L305: According to what weighting? The author should elaborate on the methods used.\nWe thank the reviewer for pointing out the missing description. Due to our revised calculation approach, we no longer rely on global mean temperatures. Therefore, we consider this comment no longer relevant.\nPage 3 / 6\n8.Appendix A: There are overlaps between the colorbar and the results. Please modify it.\nWe thank the reviewer for pointing out this issue. Based on our updated calculations, we replotted the data and ensured that no overlaps remain.", "reviewer_2": "I appreciate the author for their efforts in validating the rationality of static nominal GDP exposure. This revision has greatly improved the logical coherence of manuscript, thereby elevating its reference value for future governmental policy considerations. Overall, most of the comments have been addressed. I have only two straightforward suggestions. If the author has completed these revisions, I think this manuscript can be considered for publication in Nature Communications.\n1.P3 L86: The first apperance of the abbreviation, provide the full form of SSPs.\n2.P14 L282-L290: The first paragraph of the discussion section seems more appropriate to be clarified in the introduction section, because this manuscript is based on these existing backgrounds for an in-depth analysis."}, "author_response": "Response to Referee #2\nI appreciate the author for their efforts in validating the rationality of static nominal GDP exposure. This revision has greatly improved the logical coherence of manuscript, thereby elevating its reference value for future governmental policy considerations. Overall, most of the comments have been addressed. I have only two straightforward suggestions. If the author has completed these revisions, I think this manuscript can be considered for publication in Nature Communications.\nWe thank the reviewer for thoroughly reading the manuscript again and for providing valuable input throughout the review process.\n1.P3 L86: The first apperance of the abbreviation, provide the full form of SSPs.\nThe first appearance of SSPs has been clarified with the full form (Shared Socioeconomic Pathways). Other abbreviations have been updated accordingly.\n2.P14 L282-L290: The first paragraph of the discussion section seems more appropriate to be clarified in the introduction section, because this manuscript is based on these existing backgrounds for an in-depth analysis.\nWe thank the reviewer for this suggestion. Accordingly, we have moved the first paragraph of the Discussion section to the Introduction and clarified it to better provide the background for the in-depth analysis presented in this study.\nResponse to Referee #3\nAccept\nWe thank the reviewer for the positive feedback and for providing valuable input throughout the review process."}]

Decision letter