# Global Mapping of Concurrent Hazards and Impacts Associated With Climate Extremes Under Climate Change

Gabriele Messori, Derrick Muheki, Fulden Batibeniz, Emanuele Bevacqua, Laura Suarez-gutierrez, Wim Thiery

► To cite this version:

Gabriele Messori, Derrick Muheki, Fulden Batibeniz, Emanuele Bevacqua, Laura Suarez-gutierrez, et al.. Global Mapping of Concurrent Hazards and Impacts Associated With Climate Extremes Under Climate Change. Earth's Future, 2025, 13 (6), 10.1029/2025ef006325. hal-05237160

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# Earth's Future

## RESEARCH ARTICLE

10.1029/2025EF006325

**Key Points:**

* Under a medium-high emission scenario, compound hazard or impact occurrences will become the norm in many regions

* Under such scenario, many regions will experience specific compound occurrences for several years, or even decades, in a row

* In the absence of global climate mitigation, we project a regime shift to a world dominated by compound hazards or impacts

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

G. Messori,

gabriele.messori@geo.uu.se

**Citation:**

Messori, G., Muheki, D., Batibeniz, F., Bevacqua, E., Suarez-Gutierrez, L., & Thiery, W. (2025). Global mapping of concurrent hazards and impacts associated with climate extremes under climate change. *Earth's Future, 13*, e2025EF006325. https://doi.org/10.1029/2025EF006325

Received 22 DEC 2024

Accepted 20 APR 2025

**Author Contributions:**

**Conceptualization:** Gabriele Messori, Fulden Batibeniz, Emanuele Bevacqua, Laura Suarez-Gutierrez, Wim Thiery

**Data curation:** Derrick Muheki

**Formal analysis:** Derrick Muheki

**Methodology:** Gabriele Messori, Derrick Muheki, Fulden Batibeniz, Emanuele Bevacqua, Laura Suarez-Gutierrez, Wim Thiery

**Software:** Derrick Muheki

**Supervision:** Gabriele Messori, Wim Thiery

**Visualization:** Gabriele Messori, Derrick Muheki

**Writing – original draft:** Gabriele Messori

© 2025. The Author(s).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<figure><img src="image_2.png" /></figure>

# Global Mapping of Concurrent Hazards and Impacts Associated With Climate Extremes Under Climate Change

Gabriele Messori¹,²,³,⁴ ID, Derrick Muheki⁵, Fulden Batibeniz⁶,⁷,⁸ ID, Emanuele Bevacqua⁹ ID, Laura Suarez-Gutierrez⁸,¹⁰, and Wim Thiery⁵ ID

¹Department of Earth Sciences, Uppsala University, Uppsala, Sweden, ²Swedish Centre for Impacts of Climate Extremes (climes), Uppsala University, Uppsala, Sweden, ³Department of Meteorology, Stockholm University, Stockholm, Sweden, ⁴Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden, ⁵Department of Water and Climate, Vrije Universiteit Brussel, Brussels, Belgium, ⁶Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland, ⁷Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland, ⁸Department of Environmental Systems Science, Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland, ⁹Department of Compound Environmental Risks, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany, ¹⁰Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, Paris, France

**Abstract** Climate-related extreme events impose a heavy toll on humankind, and many will likely become more frequent in the future. The compound (joint) occurrence of different climate-related hazards and impacts can further exacerbate the detrimental consequences for society. By analyzing postprocessed data from the Inter-Sectoral Impact Model Intercomparison Project, we provide a global mapping of future changes in the compound occurrence of six categories of hazards or impacts related to climate extremes. These are: river floods, droughts, heatwaves, wildfires, tropical cyclone-induced winds and crop failures. In line with the existing literature, we find sharp increases in the occurrence of many individual hazards and impacts, notably heatwaves and wildfires. Under a medium-high emission scenario, many regions worldwide transition from chiefly experiencing a given category of hazard or impact in isolation to routinely experiencing compound hazard or impact occurrences. A similarly striking change is projected for the future recurrence of compound hazards or impacts, with many locations experiencing specific compound occurrences at least once a year for several years, or even decades, in a row. In the absence of effective global climate mitigation actions, we may thus witness a qualitative regime shift from a world dominated by individual climate-related hazards and impacts to one where compound occurrences become the norm.

**Plain Language Summary** Climate-related extreme events often result in large and negative societal impacts, and many such events are likely to become more frequent in the future. The joint occurrence of different climate-related extreme events can lead to even larger impacts than those of extremes occurring in isolation. In the absence of effective global climate mitigation to minimize the ongoing climatic change, we find that many regions worldwide transition from chiefly experiencing extreme events in isolation to routinely experiencing the joint occurrence of different climate-related extreme events. Such joint occurrences may repeatedly affect the same region for several years, or even decades, in a row.

## 1. Introduction

Climate-related events impose a heavy toll on humankind, having caused over half a million fatalities and over $2 trillion in damages globally in the first two decades of this century (CRED/DRR, 2020). These figures correspond to a sharp increase in the number of reported impactful climate-related events compared to previous decades, which can only partly be explained by better monitoring and data collection (CRED/DRR, 2020). Such a trend may instead be related to the effect of global change on climate hazards, which can in turn trigger climate-related impacts. Indeed, ample evidence shows that global change is increasing the frequency and intensity of several climate extremes, with the specific changes varying by region and extreme event type (Seneviratne et al., 2021). These upward trends are expected to continue under future global change.

Making the connection between climate extremes (included in the broader notion of hazard “drivers” sometimes used in the literature), the related hazards and the resulting impacts, however, is far from straightforward. A first challenge is conceptual. The terms extremes (or drivers), hazards and impacts are used with different meanings in the literature, and can sometimes overlap. Here we use *extremes* to refer to the values taken by one or more
**Writing – review & editing:**

Gabriele Messori, Derrick Muheki,
Fulden Batibeniz, Emanuele Bevacqua,
Laura Suarez-Gutierrez, Wim Thiery

climate variables (e.g., heavy precipitation), *hazards* to refer to the elements that trigger the impacts (e.g., flooding due to heavy precipitation), while *impacts* are the concrete societal, economic or environmental effects (e.g., fatalities due to the flooding). Nonetheless, the boundaries between these concepts are not sharp. For example, extremely hot and dry conditions are a hazard—notably for vegetation and agriculture—but also an extreme and a driver that can trigger other hazards (e.g., the spread of wildfires). To partly clarify this ambiguity, we refer to hazards that are not climate extremes themselves, but are partly or wholly driven by (extreme) climate conditions, as *derived hazards*.

Linking extremes, (derived) hazards and impacts presents several practical challenges. A key one is relating extremes to hazards. For example, heavy rainfall may or may not lead to flooding, depending on factors such as location, temporal characteristics or pre-existing conditions like soil moisture levels. Similarly, the spread of wildfires under extreme fire weather depends on a series of factors beyond the atmospheric conditions, notably the quantity and type of available fuel and the level of human firefighting interventions. A further challenge is accounting for how single hazards (and/or drivers) may combine, giving rise to compound (joint) occurrences which may result in larger impacts than those associated with the individual hazards, had they occurred in isolation (e.g., Batibeniz et al., 2023; Hochman et al., 2022; Leonard et al., 2014; Mazdiyasi & AghaKouchak, 2015; Messori et al., 2019, 2021; Zscheischler & Seneviratne, 2017). An example is the combination of flooding and severe winds during a cyclone, with the two hazards leading to potentially multiplicative effects on infrastructure damage and hindering relief efforts. The literature has explored multiple facets of compound hazards and/or drivers related to climate extremes. These include the physical links underlying compound occurrences in space and time (e.g., Lau & Kim, 2012; Littell et al., 2016; Mukherjee et al., 2020; Leeding et al., 2023), their statistical characterization (e.g., Hao et al., 2018; Zscheischler et al., 2021), and their climatology and trends (e.g., Li et al., 2022; Messori et al., 2024). Understanding changes in the frequency and/or severity of compound occurrences requires analyzing both changes in their individual components and changes in their dependence structure (e.g., Zscheischler & Seneviratne, 2017).

Finally, hazards need to be related to impacts, from fatalities to financial loss, environmental damage and more. These impacts depend on the physical characteristics of the hazards, on the exposure of people and human or natural assets to the hazards, and on their vulnerability. The latter is shaped by both natural and socioeconomic factors, which can be highly localized. Examples include soil and landcover type as natural modulators of flood severity and impacts (Hümann et al., 2011), preconditioning or legacy effects from exposure to previous hazards (e.g., Bastos et al., 2021; Wu et al., 2022) and power relations and human agency as modulators of differential climate impacts on societies (Rusca et al., 2021, 2023). As highlighted by Fehlman et al. (2025), notwithstanding the ample literature on compounding hazards and/or drivers related to extreme climate events, comparatively few studies have investigated the impacts of such compound occurrences.

While not exhaustive, the above overview illustrates the complexity of studying hazards and impacts related to climate extremes. The rapid ongoing global change highlights the urgency of assessing future changes in these hazards and impacts. This task is particularly challenging, because it requires developing a model chain where numerical simulations from Global Climate Models (GCMs) under different socio-economic development scenarios serve as input for impact models. As a result, a large part of the future-looking literature has focussed on projections of single or compound climate extremes. This body of research has considered different dimensions of compound occurrences, including temporal clustering of extremes (e.g., Vogel et al., 2020); co-occurrent extremes (e.g., Bevacqua et al., 2022; Rutgersson et al., 2022; Ridder et al., 2022; Wu et al., 2023); representation of different composite climate indices (e.g., Adeyeri et al., 2022); and compound drivers (e.g., Hermans et al., 2024; Lopez-Marti et al., 2025). A smaller body of work has included exposure to the extremes (e.g., Batibeniz et al., 2020, 2023; Jones et al., 2015; Spinoni et al., 2021; Weber et al., 2020; Yang & Zhao, 2024), or examined projections of derived hazards (e.g., AgaKouchak et al., 2020; Alfieri et al., 2017; Kloster & Lasslop, 2017) or impacts (e.g., Jägermeyr et al., 2021; Quijal-Zamorano et al., 2021; Severino et al., 2024)—the latter skewed toward crop yields and health impacts. Notably, there is comparatively little work explicitly considering future compound derived hazards (e.g., Bloomfield et al., 2024; Gu et al., 2022; Muheki et al., 2024) or impacts resulting from future compound hazards (e.g., Jin et al., 2017; Slater et al., 2022). According to our definition of hazards, we do not consider for example, compound flooding as a compound hazard, but rather as a single derived hazard resulting from multiple extremes, such as extreme winds and extreme precipitation. Climate projections suggest that many future compound climate extremes may be unprecedented in severity and intensity (Dosio et al., 2023; Seneviratne et al., 2021), with global change affecting both the physical and statistical links between different
hazards and/or drivers (e.g., Toreti et al., 2019; Zscheischler & Seneviratne, 2017). Investigating compound (derived) hazards and the related impacts thus holds clear societal and economic value.

In this study, we provide a global mapping of future changes in the compound occurrence of a set of six hazards and impacts associated with climate extremes. Hereafter, we use the term *compound* to indicate the co-occurrence of two or more hazards or impacts at the same location. Specifically, we consider river floods, droughts, heat-waves, wildfires, tropical cyclone-induced winds and crop failures. To study these, we leverage postprocessed data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) process-based impact models, which take as input GCM simulations under different socioeconomic development scenarios. The data set comprises multiple GCM inputs and impact models for each hazard or impact category.

Our analysis does not fully address the previously outlined challenges, since we chiefly consider (derived) hazards. Crop failure is the only category that strictly matches our definition of impact. Nonetheless, as previously noted, there is no univocal definition of extremes (or drivers), hazards and impacts in the literature, and previous work has termed all hazards and impacts that we consider here: “extreme climate impact events” (Lange et al., 2020). While we attempt to use a precise terminology in this study, we thus recognize that the use of the different terms remains to some extent subjective. On this basis, we analyze jointly crop failure and (derived) hazards. We specifically seek to quantify how multiple climate-related compound (derived) hazards and impacts have changed and will change in the future on a global scale. As highlighted above, this is a little-explored yet highly societally relevant facet of global change. We further seek to relate any changes we observe to changes in the frequency of and dependence between the individual hazards and impacts, and to understand how changes in compound occurrences scale with mean global warming levels. The latter is motivated by the fact that much of the global climate policy action builds on temperature targets. Our work clearly advances the knowledge from previous literature focusing on compound hazards and/or drivers derived exclusively from GCM output, as the ISMIP data enables us to leverage a combined GCM and impact model chain. This makes our work unique in a global compound hazard/impact perspective. For example, we analyze river floods and wildfires, as opposed to using precipitation or fire weather indices derived from GCM data as proxies for flooding or wildfire risk. A second innovative feature of our work is that we sample three different uncertainty sources: inter-model differences between GCMs, inter-model differences between impact models, and scenario uncertainty. Finally, unlike work centered on specific derived compound hazards or related impact categories, our study provides a global and multi-hazard/impact investigation.

## 2. Data and Methods

### 2.1. Data

We use the data set originally developed by Lange et al. (2020), and extended by Thiery et al. (2021a, 2021b). The data set is derived from the ISIMIP2b model simulations (Frieler et al., 2017) and was recently used in a regional compound hazard and impact analysis by Muheki et al. (2024). This data set provides global yearly areal exposure to river floods, droughts, heatwaves, wildfires, tropical cyclone-induced winds and crop failures with a 0.5° × 0.5° horizontal resolution. The occurrence of a hazard or impact in a given year refers to its occurrence on at least one day during that year. We consider both historical simulations, covering the period 1861–2005, and future projections, covering the period 2006–2099. We divide the historical period into an early industrial period (1861–1910) and a present-day period (1956–2005). For future projections, we consider an end-of-century period (2050–2099) under Representative Concentration Pathways (RCPs) 2.6, 6.0, and 8.5.

The climate projections come from the GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 models, part of phase five of the Coupled Model Intercomparison Project (CMIP5). The model output was bias-adjusted prior to serving as input for the impact models (Frieler et al., 2017). For river floods, we employ the CLM45 (Lawrence et al., 2011), H08 (Hanasaki et al., 2018), JULES-W1 (Best et al., 2011), LPJmL (Schaphoff, Forkel, et al., 2018; Schaphoff, von Bloh, et al., 2018), MPI-HM (Stacke & Hagemann, 2012), ORCHIDEE (Guimberteau et al., 2018), PCR-GLOBWB (Wada et al., 2016), WaterGAP2 (Müller Schmied et al., 2014, 2016) and CaMa-Flood (Yamazaki et al., 2011, 2013) models. For droughts, we use the same models except for CaMa-Flood; for crop failures GEPIC (Folberth et al., 2012), LPJmL and PEPIC (Liu et al., 2016); for wildfires CARAIB (Dury et al., 2011), LPJ-GUESS (Smith et al., 2014), LPJmL and VISIT (Ito & Inatomi, 2012); and for tropical cyclone-induced winds KE-TG-meanfield (Emanuel, 2013; Holland, 2008).
The ISIMIP2 set of simulations has been extensively validated (Rosenzweig et al., 2017), and the different impact models present both strengths and limitations in simulating the hazards and impacts that we consider here under global change. A validation of several of the hydrological models that we build upon is provided in Huang et al. (2017), Hattermann et al. (2018), Zaherpour et al. (2018), and Mester et al. (2021). The former study underscores the generally satisfactory performance of the models in simulating ensemble discharge statistics, except for extremes in particular basins. Hattermann et al. (2018) note the important role of the uncertainty related to climate model input, while Zaherpour et al. (2018) outline the limitations of the hydrological models in reproducing the timing and magnitude of the seasonal cycle, with location-dependent biases. The latter study further highlights temporal aggregation on annual timescales as an approach to obtain more robust information from the models. Finally, Mester et al. (2021) focus specifically on flood extent, and find generally strong inter-model agreement, albeit with some exceptions where specific models overestimate flood extend. Gu et al. (2019), analyzed the performance of hydrological models in simulating soil moisture—which we use here as an indicator of drought. They found a good spatial agreement of the ISIMIP data with other observational and model-generated data, although the ISIMIP data tended to underestimate drying trends in the historical period. Evaluations of the ISIMIP crop models provide varied evidence. Mistry et al. (2017) find that the models overestimate the weather sensitivity of yields compared to the observational record, and generally display a heterogeneity of responses. Heinicke et al. (2022) instead argue for the crop models underestimating yield responses to droughts and heatwaves. The apparent contradiction may be due to the focus on specific extreme events versus a bulk analysis of climatic variability, and to the fact that Mistry et al. (2017) focus exclusively on the USA. The performance of the wildfire modes was evaluated in Lange et al. (2020; see their Text S3), who found a low inter-model agreement and that different models under- or overestimate historical burnt area. The dynamical down-scaling and wind field models used to derive tropical cyclone-induced winds were validated in Geiger et al. (2018), showing good agreement with observed tropical cyclone data.

We derive the exposed area for each hazard/impact category we consider from the impact model output as described in Lange et al. (2020). The one exception to the above model chain is heatwaves, whose occurrence is diagnosed directly from the GCM output, following the HWMId as defined by Russo et al. (2015, 2017). River floods, wildfires and tropical cyclone-induced winds are assigned a fractional area coverage of each gridbox, while heatwaves, droughts and crop failures affect by design entire gridboxes. We consider a gridbox to be affected by the former three hazards if more than 0.5 % of the gridbox area is affected. This value was chosen as the three hazards are sometimes highly localized, and using larger area percentages led to overlooking several hazard occurrences.

Table 1 illustrates the full set of impact models used, and the definitions of the different hazard/impact categories. Crop failure simulations are not available for the RCP8.5 scenario. We adopt a multi-model ensemble approach, combining outputs from the different impact models to diagnose the occurrence of each hazard/impact category. To ensure the physical consistency of our results, the compound occurrences are identified from different impact models all forced by the same GCM.

## 2.2. Methods

To map future changes in hazard/impact occurrences, we leverage a number of statistical metrics. These include: (1) the probability of occurrence of individual or compound hazards/impacts; (2) a recurrence metric; (3) a propensity metric (Equation 1); (4) the co-occurrence ratio (Equation 2); and (5) a decomposition of the drivers of changes in compound occurrences. We compute these metrics on each GCM-impact model pair, and then we average the results across all models. The only exception is recurrence, for which we compute the percentile across all GCM-impact model pairs. For propensity and the co-occurrence ratio, we also repeat the analysis by excluding crop failures and considering only derived hazards.

The above statistical metrics reflect the paper's main goal of quantifying past and future changes in multiple climate-related compound (derived) hazards and impacts. Specifically, the probability of occurrence and the recurrence both quantify changes in compound occurrences and enable a comparison to changes in individual occurrences. The propensity relates the frequency of individual hazards/impacts to that of all hazard/impacts, thus enabling linking the changes in compound occurrences to the relative importance of their individual components. The co-occurrence ratio illustrates where and when compound occurrences dominate over individual hazards/impacts, elucidating the implications of changes in compound occurrence frequency. Finally, the decomposition
<table><caption>**Table 1**
The Impact Models and Hazard/Impact Categories Used in This Study (Adapted From Muheki et al., 2024)</caption><thead><tr><th>Hazard/impact category</th><th>Definition following Lange et al. (2020)</th><th>Impact models</th></tr></thead><tbody><tr><td>River floods</td><td>Daily river flow within a gridbox greater than the 100-year return period flow during pre-industrial times.</td><td>CLM45, H08, JULES-W1, LPJmL, MPI-HM, ORCHIDEE, PCR-GLOBWB, WaterGAP2, CaMa-Flood</td></tr><tr><td>Heatwaves</td><td>Daily Heat Wave Magnitude Index (HWMId) exceeding the 99th percentile of the HWMId during pre-industrial times. The HWMId is defined as the annual maximum heatwave magnitude, where a heatwave consists of a minimum of three consecutive days with temperatures above the 90th percentile of daily temperatures, centered on a 31-day window, between 1981 and 2010 (Russo et al., 2015, 2017).</td><td>HWMId99 (directly diagnosed from GCMs)</td></tr><tr><td>Droughts</td><td>Drop in soil water content below the 2.5th percentile of the distribution during pre-industrial times considering periods longer than 6 months. Uses monthly soil moisture at different soil layer depths (as close as possible to 100 cm) depending on the impact model used (see Text S2 in Lange et al., 2020).</td><td>CLM45, H08, JULES-W1, LPJmL, MPI-HM, ORCHIDEE, PCR-GLOBWB, WaterGAP2</td></tr><tr><td>Crop failures</td><td>Drop in crop yield below the 2.5th percentile of the distribution during pre-industrial times.</td><td>GEPIC, LPJmL, PEPIC</td></tr><tr><td>Wildfires</td><td>Total annual burnt area.</td><td>CARAIB, LPJ-GUESS, LPJmL, ORCHIDEE, VISIT</td></tr><tr><td>Tropical cyclone-induced winds</td><td>Exposure to tropical cyclone-induced winds (wind speed ≥64 kn) sustained for at least 1 min.</td><td>KE-TG-meanfield</td></tr></tbody></table>

of the drivers of change supports understanding why the changes quantified by the other metrics occur. All metrics enable comparisons across the different individual and compound hazards/impacts.

1. **Probability of occurrence of individual or compound hazards/impacts.** The yearly occurrence of individual hazards/impacts is expressed as a probability over [0, 1]. A value of 0 implies no occurrence at a given gridbox, while one implies that the hazard/impact has occurred during every year in the analyzed data. A value of 0.5 indicates occurrence of the hazard/impact in 25 years out of the 50 years in each period. We quantify in the same way the joint occurrence of pairs of hazards/impacts at single gridboxes.

2. **Recurrence.** We consider whether individual hazards/impacts or specific pairs of hazards/impacts recur in time by counting stretches of consecutive years displaying occurrences at each gridbox. We then compute the gridbox-level 95th percentile separately for impact model output driven by each GCM, and finally average across GCMs. We term this quantity the *recurrence*.

3. **Propensity.** The propensity metric at a given gridbox is given by:

$$P_i = 1 + \ln\left(\frac{n_i}{n_{\text{all}}}\right) \qquad (1)$$

where $i = 1, 2, ... 6$ refers to the six hazard/impact categories considered here, $n_i$ is the number of occurrences of hazard/impact category $i$, and $n_{\text{all}}$ is the average number of occurrences across all categories, namely the sum of all occurrences at that gridbox divided by 6. $P > 1$ means that a given hazard/impact is more frequent than the average of all six categories at a given gridbox, and vice-versa for $P < 1$. $P = 2$ corresponds to a given hazard/impact occurring ~2.7 times more often than average.

4. **Co-occurrence ratio.** The co-occurrence ratio at a given gridbox is given by:

$$CR = 1 + \ln\left(\frac{n_{\text{ext compound}}}{n_{\text{ext single}}}\right) \qquad (2)$$

Where $n_{\text{ext compound}}$ refers to compound occurrences and $n_{\text{ext single}}$ refers to the occurrence of single hazards/impacts in a given gridbox. Each extreme within a compound occurrence is counted individually to determine $n_{\text{ext compound}}$. $CR > 1$ means that there are more compound than isolated occurrences, and vice-versa for
CR < 1. CR = 2 corresponds to compound occurrences being ~2.7 times more frequent than isolated occurrences.

5. *Decomposition of the drivers of changes in compound occurrences.* We decompose the modeled changes in pairs of hazard/impact occurrences at a given gridbox by considering the contributions from changes in each co-occurring hazard/impact and from changes in their dependence (Bevacqua et al., 2020; Muheki et al., 2024). The term dependence is used here in its statistical meaning, and does not imply physical links or causal connections. The calculation builds on the well-established probability ratio approach (e.g., Philip et al., 2022), using the early industrial period (1861–1910) as a reference. Full details are provided in Appendix Text S2 in Supporting Information S1. Since we apply this approach to the binary occurrence of impacts/hazards, the changes in dependence should be interpreted with care when changes in the probability of one or both hazards/impacts constituting the pair of interest are large.

## 3. Results

### 3.1. Global Overview of Individual and Compound Hazards and Impacts

The different hazard/impact categories that we consider display varying probabilities and preferred areas of occurrence. During the present-day period, wildfires are the most frequent category, affecting all continents (Figure 1j). Under a medium-high future emission pathway, heatwaves increase dramatically, becoming by far the dominant hazard at global scale (Figures 1h and 1i). Wildfires continue to be a frequent hazard but their increase in occurrence, while geographically widespread, is not comparable to that of heatwaves (cf. Figures 1i and 1l). Other hazards also show large increases in occurrence, for example, river floods in both the tropics and the Northern high latitudes (Figure 1c) and droughts, notably around the Mediterranean (Figure 1f). Crop failures display marked increases in Africa, Southern Asia and the Americas, and a moderate decrease in Eastern Europe (Figure 1r). The corresponding probabilities of occurrence for the early industrial period and for the end-of-century under additional scenarios are provided in Figure S1 in Supporting Information S1.

We further investigate the recurrence of the different hazards/impacts at single locations (Figure 2; Section 2.2). In the present climate, most of the hazards/impacts recur comparatively rarely. Only wildfires, and to some extent droughts in North Africa, show prolonged periods of continuous occurrence (Figures 2d and 2j). In a medium-high future emission pathway, heatwaves become the norm across much of Africa, South and Central America, Australia, the Mediterranean and southern Asia, displaying a marked increase in recurrence compared to the present day (Figures 2h and 2i). Periods of consecutive occurrence of wildfires also become longer in many regions (Figure 2l), yet wildfire frequency increases less on average than heatwave frequency. Already in a medium-high emission scenario, both heatwaves and wildfires thus become the norm in many regions. Other hazards also show future increased recurrence, albeit mostly at regional scale. The corresponding recurrences for the early industrial period and for the end-of-century under additional scenarios are provided in Figure S2 in Supporting Information S1.

The probability of occurrence and recurrence of the different hazards/impacts provide a global view of the dominant categories. However, they do not explicitly inform on the relative importance of occurrences of different hazards/impacts at specific locations, and how this is projected to change in the future. To investigate this, we compute the propensity metric (Section 2.2), shown in Figure 3. In the present day, wildfire is the hazard showing high propensity values over the largest spatial extent, followed by droughts, heatwaves and, to a lesser extent, river floods (Figures 3a, 3d, 3g, and 3l). In other words, there are vast areas where one or more of these hazards dominate the local hazard/impact landscape in terms of occurrence. The other hazard/impact categories only show limited regions of high propensity. Looking into the future, droughts and river floods lose relative importance in many regions, at the expense of heatwaves (Figures 3f and 3i). The latter become comparable to wildfires in terms of having the largest areas of high propensity (cf. Figures 3h and 3k). This does not mean that the drought or river flood hazards necessarily decrease in the future, but rather that increases in other hazards, notably heatwaves, outpace those of droughts and river floods so that the latter become less important in relative terms. Figure S3 in Supporting Information S1 displays the corresponding propensities for the early industrial period and for the end-of-century under additional scenarios. Figures S12 and S13 in Supporting Information S1 display propensity computed for derived hazards only, namely excluding crop failures. The exclusion of crop failure results in minor differences where this impact has a high propensity, such as parts of Europe, of eastern North America and regionally in Asia.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Probability of occurrence for individual hazard/impact categories during: (left column) the present day (1956–2005), (middle column) the end of century (2050–2099) under RCP6.0, and (right column) difference between the two. Note that the color range for HW and WF differs from that for all other hazard/impact categories. Grayed-out gridboxes indicate locations not covered by the data set.</figcaption></figure>

We next consider the importance of compound hazards/impacts at the global scale. The co-occurrence ratio of the hazards/impacts during the historical period is generally low, with values around zero at most locations (Figure 4a). This indicates a predominance of isolated hazards/impacts relative to compound ones. The exception are values up to around one at some coastal locations, such as south-eastern Indochina, south-eastern Africa or western Central America. This is primarily the result of tropical cyclone-induced winds co-occurring with one of the other categories. The RCP6.0 scenario shows a clear shift to a world dominated by compound hazards/impacts (Figures 4b and 4c). This is particularly evident in the tropics and the lower midlatitudes, with the highest co-occurrence ratio values simulated in Africa, Central and South America, the Mediterranean region, South-East Asia and Northern Australia. The corresponding co-occurrence ratios for the early industrial period and for the end-of-century under additional scenarios are provided in Figure S4 in Supporting Information S1. Figures S14 and S15 in Supporting Information S1 display the co-occurrence ratio computed for derived hazards only, namely
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Recurrence for individual hazard/impact categories during: (left column) the present day (1956–2005), (middle column) the end of century (2050–2099) under RCP6.0, and (right column) difference between the two. Grayed-out gridboxes indicate locations not covered by the data set or locations at which the specified hazard/ impact did not occur.</figcaption></figure>

excluding crop failures. This results in lower co-occurrence values where crop failures display comparatively frequent occurrences, such as parts of Europe, of eastern North America and regionally in Asia.

**3.2. Investigating Specific Compound Hazards and Impacts**

The results in Section 3.1 evidence a marked increase in both some individual hazard/impact categories and in compound hazard/impact occurrences. We next investigate in more detail specific pairs of hazards and impacts.

First, we consider all the possible pairs of our six hazard/impact categories, identifying the most frequently occurring one at each land gridbox (Figure 5). In the early industrial period, droughts and wildfires, river floods and wildfires and crop failures and wildfires dominate across large parts of the globe. Already during the present day, pairs including heatwaves start to dominate large areas, notably the combination of heatwaves and wildfires. However, during both periods compound occurrences are rare, and most hazards/impacts occur in isolation
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Propensity metric for individual hazard/impact categories during: (left column) the present day (1956–2005), (middle column) the end of century (2050–2099) under RCP6.0, and (right column) difference between the two. Grayed-out gridboxes indicate locations not covered by the data set or at which the specified hazard/ impact did not occur.</figcaption></figure>

(Figure 4; Figure S4 in Supporting Information S1). The two future scenarios show an almost complete domi-
nation by the heatwave and wildfire and drought and heatwave pairs, consistent with the rapid increase in
heatwaves shown in Figure 1 and the similarly rapid expansion of regions where their importance relative to other
hazards/impacts becomes dominant (Figure 3).

Next, we examine annual average land surface percentage affected by each of the compound hazard/impact pairs. Indeed, identifying the most frequent pair at each location does not directly inform on the land surface area affected by each pair. In the early industrial and present climates, in line with the results from the previous sections, none of the compound pairs cover more than a few percent of the global land surface. For all pairs, higher future global warming levels correspond to unchanged or larger multi-model mean and median percentages of affected land area (Figure 6). The increase is particularly evident for all pairs including heatwaves and for some pairs including crop failures. The pair with the largest percentage increase is river floods and heatwaves (with the
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Co-occurrence ratio during: (a) the present day (1956–2005), (b) the end of century (2050–2099) under RCP6.0, and (c) difference between the two. Grayed-out gridboxes indicate locations not covered by the data set or where no single hazards/impacts occurred.</figcaption></figure>

median increasing by a factor of over 24 from present-day to RCP2.6, and by a factor of over 185 for RCP8.5). In terms of absolute values, the combination of heatwaves and wildfires dominates for all future scenarios, affecting over a quarter of the global land surface in RCP8.5. We next take the RCP6.0 scenario as a representative medium-high emission pathway, and rank the other pairs based on median values. Heatwaves and crop failures rank second, followed by droughts and heatwaves, river floods and heatwaves and river floods and wildfires. The latter is the first pair not including heatwaves which, as highlighted in Figure 3, display a rapidly increasing relative importance globally.

Based on these results, we focus the remaining analysis on the heatwaves and wildfires, heatwaves and crop failures, droughts and heatwaves, and river floods and heatwaves pairs, and on the RCP6.0 scenario. We show the river floods and wildfires, droughts and wildfires and crop failures and wildfires pairs—namely all the pairs that in RCP6.0 affect ≥1% of the global land surface—in Figures S5–S11 in Supporting Information S1.

Heatwaves and wildfires, heatwaves and crop failures, droughts and heatwaves, and river floods and heatwaves pairs all show future increases in occurrence frequency in multiple regions (Figure 7). As previously noted,

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Most frequent compound hazard/impact pairs per location during: (a) the early industrial period (1861–1910), (b) the present day (1956–2005), and the end of century (2050–2099) under (c) RCP2.6 and (d) RCP6.0 scenarios. We do not display RCP8.5 as crop failure is not available for that scenario. The abbreviations in the legend are as follows: river floods (RF), wildfires (WF), heatwaves (HW), crop failures (CF), droughts (DR), and tropical cyclone-induced winds (TC). White gridboxes indicate locations not covered by the data set or where no compound hazard/impact pairs occurred.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Boxplots of annual average land surface percentage across the different models affected by each of the 15 pairs of compound hazards/impacts during the early industrial (1861–1910), present-day (1956–2005) and end-of-century (2050–2099) periods. For the end-of-century period, RCP 2.6, 6.0, and 8.5 are shown. The abbreviations are as in Figure 5. The box edges show the upper and lower quartiles of the multi-model distribution; the horizontal black lines the multi-model medians, the circles the multi-model means and the whiskers the range of values within 1.5 interquartile ranges of the box edges.</figcaption></figure>

heatwaves and wildfires stand out for the extent and magnitude of the changes, which span virtually the whole globe except for glaciated and desert areas (Figure 7c). Droughts and heatwaves also show a geographically widespread increase in occurrence frequency, albeit with smaller changes than the heatwaves and wildfires pair (Figure 7i). The other two pairs show large increases at regional level, notably sub-Saharan Africa, southern Asia and parts of Northern central and South America for heatwaves and crop failures, and river basins in the tropics and the northern High Latitudes for river floods and heatwaves (Figures 7f and 7l). The corresponding results for other time-periods, scenarios and hazard/impact pairs are shown in Figures S5–S7 in Supporting Information S1. The less frequent pairs also show regionally marked increases in occurrence, with concurrent wildfires and crop failures becoming commonplace in sub-Saharan Africa, the northern Indian subcontinent, Central America and north-eastern China already in the RCP6.0 scenario (Figure S6 in Supporting Information S1).

Coinciding with the widespread increase in compound occurrences of hazard/impact pairs, we also find an increase in their recurrence (Section 2.2). The largest increases are observed in the global tropical and subtropical belt for heatwaves and wildfires, whose compound occurrence becomes the norm in a future world (Figures 8a–8c). Heatwaves and crop failures display large increases crop in agriculturally important regions such as Northern India, Pakistan, Mesopotamia, West Africa and parts of the Americas (Figure 8f). North Africa and Western Asia display large increases in the recurrence of compound drought and heatwave occurrences (Figure 8i). Notably, we observe virtually no negative changes in recurrence for any of the pairs analyzed here. The corresponding results for other time-periods, scenarios and hazard/impact pairs are shown in Figures S8–S10 in Supporting Information S1. Future changes in recurrence are modest for the less frequent pairs. Indeed, even if their occurrence frequency increases, they remain sufficiently rare to seldom occur over several consecutive years.

The main driver of the increased occurrence of the hazard/impact pairs analyzed here is the rapid increase in the occurrence of heatwaves (Figure 9). The increased occurrence of other hazards/impacts also plays a relevant role. This can match that of the heatwaves on a regional scale, for example, river floods in Northern Eurasia (Figure 9l) but is not comparable to the latter on a global level. Changes in the dependence between the hazards/impacts often display values < 1, and thus does not play a role in the observed increase in compound occurrences. This holds
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Probability of joint occurrence for the heatwaves and wildfires, heatwaves and crop failures, droughts and heatwaves, and river floods and heatwaves pairs. Values are shown for: (left column) the present day (1956–2005), (middle column) the end of century (2050–2099) under RCP6.0, and (right column) the difference between the two. The abbreviations are as in Figure 5. Grayed-out gridboxes indicate locations not covered by the data set.</figcaption></figure>

even at regional level, since at the few locations showing dependence changes >1 these are overshadowed by the increases in the individual hazards/impacts. The corresponding results for other hazard/impact pairs are shown in Figure S11 in Supporting Information S1. For these less frequent pairs, changes in dependence can play a regionally important role, for example, enhancing the compound occurrence of wildfires and crop failures in Eastern Europe and the Indian subcontinent, and the occurrence of droughts and wildfires again in the Indian subcontinent.

**4. Discussions and Conclusion**

In this study, we provide a global mapping of future changes in the individual and compound (joint) occurrences of six hazards and impacts associated with climate extremes. Wildfires are one of the most frequent hazards in the current climate, and continue to be so also in the future. This is in line with evidence pointing to the human imprint on historical burned area (Burton et al., 2024) and to large future increases in global wildfire frequency driven by meteorological factors, although this may be partially counterbalanced by other factors including land-use change (e.g., Huang et al., 2015; Bowman et al., 2020). Heatwaves are comparatively rarer in the present climate, but their occurrence and importance relative to other hazards increases sharply in future climates. This is consistent with literature finding that heatwaves will become more frequent, persistent and intense over the bulk of the globe (Domeisen et al., 2023; Seneviratne et al., 2021). Other hazards also show increases, for example, soil moisture droughts around the Mediterranean. The increased drought hazard in these areas is consistent with Cook et al. (2020), who show that CMIP6 models estimate a robust regional drying of both the surface and total column soil moisture, while model agreement is lower elsewhere. Nonetheless, the increase in heatwaves dominates the picture.
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Recurrence for the heatwaves and wildfires, heatwaves and crop failures, droughts and heatwaves, and river floods and heatwaves pairs. Values are shown for: (left column) the present day (1956–2005), (middle column) the end of century (2050–2099) under RCP6.0, and (right column) the difference between the two. The abbreviations are as in Figure 5. Grayed-out gridboxes indicate locations not covered by the data set or locations at which the specified hazards/impacts did not occur.</figcaption></figure>

Our results further point to a qualitative regime shift in the occurrence of the six hazard/impact categories we consider. As we move from the present-day climate to a medium-high emission pathway, many regions in the world transition from primarily experiencing individual hazard/impact occurrences to routinely experiencing compound occurrences. This shift is chiefly associated with hazard/impact pairs including heatwaves, though other pairs such as crop failures and wildfires also rapidly grow in importance. Such findings align with the general conclusion that anthropogenic climate change increases the risk of compound hazards (e.g., AgaKouchak et al., 2020; Bevacqua et al., 2022; Ridder et al., 2022). Global change may affect the dependence structure between different hazards and/or hazard drivers (Zscheischler & Seneviratne, 2017), potentially also altering the underlying physical processes (Toreti et al., 2019). We however find in our analysis that the increase in compound occurrences is chiefly driven by the rapid increase in occurrence of the individual hazards/impacts, and most notably by the increase in heatwave occurrence. From a purely statistical perspective, the rapid increase in the occurrence of one or both of the hazards/impacts in a pair increases the probability of compound occurrences. Changes in the processes underlying the compound occurrences would instead be expected to emerge through changes in the dependence between the individual hazards/impacts. The fact that we do not observe this may result from the long timescales that we consider to define compound occurrences, which are relevant from an impacts perspective but may obfuscate physically driven dependence changes. We also reiterate the limitation of using binary variables (Sect. 2.2), and underscore that dependence changes can vary considerably across models. Because it remains relatively rare for many hazard pairs to compound in current climate conditions, unprecedented or unforeseen (cascading) impacts may arise in a future where compound hazards become the norm.

A simple analysis of the percent increases in compound occurrences further reveals a strong nonlinearity with the level of global warming. The present-day climate and the three future scenarios (RCPs 2.6, 6.0, and 8.5) considered here correspond to mean global warming levels of +0.4 K, +1.8 K, +2.6 K, and +3.7 K respectively,
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Determinants of change in the probability of joint occurrence of the heatwaves and wildfires, heatwaves and crop failures, droughts and heatwaves, and river floods and heatwaves pairs. Values are shown for the end of century (2050–2099) under RCP6.0 relative to the early industrial period (1861–1910). We illustrate the probability ratio assuming only changes in one of the hazards/impacts per pair (left and middle columns) or in the dependence of two co-occurring hazards/impacts (right column). Probability Ratios >1 represent a contribution to increased compound occurrences, while Probability Ratios <1 represents a contribution to decreased compound occurrences. The abbreviations are as in Figure 5. Grayed-out gridboxes indicate locations not covered by the data set or locations at which the specified hazard/impacts in the pairs did not co-occur during one or both of the periods.</figcaption></figure>

relative to the early industrial period. These global warming levels are determined using ISIMIP global mean temperature anomalies over the full duration of each 50-year period. However, the number of compound occurrences increases by 70%, 501%, 942%, and 1503%, respectively. Crop failure is not available for the RCP8.5 scenario, and we therefore do not include it in the calculation of the percentage increase there. Normalizing to the changes between the early industrial and the present-day periods, the mean global warming levels are thus: 1, 4.5, 6.5, and 9.3, while the increases in compound occurrences are: 1, 7.2, 13.5, and 21.5. A similarly striking change is found when looking at the recurrence of compound hazards or impacts. In the present day, compound occurrences seldom recur in consecutive years at the same location. In the future, stretches of one or more decades where a given location experiences specific compound occurrences at least once a year will become commonplace.

Our analysis and the data set we leverage come with several caveats. First, the data lacks crop failure information for RCP8.5, hindering direct comparison of all three future scenarios we consider here. Second, the impact model simulations rely on CMIP5 GCMs as input, rather than on the newer CMIP6 models. Although trends in several extreme events are consistent across these two generations of climate models—notably for heatwaves, heavy precipitation and drought (e.g., Cook et al., 2020; Seneviratne & Hauser, 2020)—differences exist. Specifically, CMIP5 models may underestimate changes in a number of climate hazard indices relative to CMIP6 (Coppola et al., 2021). Third, our comparatively small set of input GCMs likely undersample both the climate system's internal variability and the intermodel spread of the full set of CMIP models. Fourth, the ISIMIP2 impact model data we build on has been subject to an extensive validation process (see Section 2.1), which has highlighted
shortcomings in simulating specific hazards and impacts. This is notably the case for the response of crop models to heatwaves and droughts (Heinicke et al., 2022) and for the performance of the wildfire models in simulating burnt area (Lange et al., 2020). These shortcomings are however not unique to the models that we consider here (e.g., Schewe et al., 2019). A final important caveat is the yearly time resolution of the data. A given hazard/ impact occurs at a given gridbox if that gridbox experiences the hazard/impact at least once during the year. The data does not inform on the timing of the occurrence during the year, nor does it differentiate between single and multiple occurrences. This choice was made in Lange et al. (2020) to facilitate the aggregation of exposure across hazard/impact categories, since crop failure is typically diagnosed at growing-season timescale and droughts at multi-month timescales. It is nonetheless suboptimal both in terms of aggregating over fixed calendar periods as opposed to a sliding time window and when studying other hazards which occur on shorter timescales.

Nevertheless, the data set we use is the most extensive currently available multi-hazard/impact data set derived from post-processed global impact models, and no comparable data set is available which leverages CMIP6 simulations. This indirectly highlights the complexity of implementing a multimodel GCM—impact model chain and then postprocessing the impact model output. The use of post-processed impact model data however provides a unique perspective on the occurrence of compound hazards and impacts associated with climate extremes, beyond what can be obtained from GCM output. For example, we analyze postprocessed output relating directly to river floods and wildfires, as opposed to using precipitation or fire weather indices as proxies for flooding or wildfire risk. Moreover, the GCMs included in the analysis, although limited in number, were bias corrected (Frieler et al., 2017) and were selected to be representative of the CMIP5 distribution of equilibrium climate sensitivity (Lange et al., 2020). The use of a standardised protocol across different GCM-impact model simulations enables a multi-model analysis of compound hazards/impacts that is seldom conducted in the impact modeling literature. This enables us to sample three important uncertainty sources, namely inter-model differences between GCMs, inter-model differences between impact models, and scenario uncertainty. Such an assessment is key for understanding the range of future risk (Lehner et al., 2020), and we see this work as an important step toward the gold standard of sampling jointly inter-model, scenario and internal variability uncertainty in hazard/impact risk studies.

The long timescales that we implicitly consider in our definition of compound occurrences result in all hazards/impacts contributing to the yearly exposure of a given location. Even though several of the studied hazards occur on sub-annual timescales, their compounding on a yearly basis can nonetheless be supported by physical processes and be relevant from an impact perspective. Indeed, the literature has explicitly highlighted the potentially major impacts of chains of temporally consecutive hazards (de Ruiter et al., 2020). Examples relevant to the categories and timescales that we consider here include the role of droughts in shaping subsequent floods (Barendrecht et al., 2024), wildfires and floods compounding across seasons (Liu et al., 2022; Yu et al., 2023) and early season heatwaves affecting crop yields in the following months (e.g., Fontana et al., 2015; Ullah et al., 2024). Similarly, some of the hazards we consider can provide favorable long-term background conditions for other hazards occurring on shorter timescales, which in turn can feed back on the first hazard. An example is the well-established link between soil moisture droughts and heatwaves (e.g., Hirschi et al., 2011). Nonetheless, we recognize that not all of the hazard and impact pairs that we consider are physically linked on long timescales. At the same time, events that are physically unrelated can also have compound impacts, motivating their joint analysis. An example are droughts following tropical cyclone-induced winds. The occurrence of the drought is not favored by the preceding tropical cyclone, yet the cyclone can increase the vegetation's vulnerability to drought, thus exacerbating the drought's impacts (Smith-Martin et al., 2022).

Concerning our methodological approach, the statistical metrics that we use depend, by nature, on the different thresholds employed to define when an individual hazard or impact occurs. These thresholds were selected in Lange et al. (2020) and Thiery et al. (2021a) to reflect societally and environmentally impactful events, but we recognize that the choice is to some extent subjective. Similar reasoning applies to our choice of a fractional area threshold for deciding when a given gridbox is affected by the hazards for which we have areal coverage information. On a conceptual level, we have focussed on areal exposure, without accounting for the severity of the hazards considered, nor for population, infrastructure or ecosystem exposure or vulnerability, nor for adaptation processes. These components would be needed to make the final step between the hazards we consider and impacts. Integrating the above elements into an analysis of compound hazards and the associated impacts is far from simple. Future demographic projections are readily available (e.g., Kc & Lutz, 2014), but integrating these with coherent projections of other exposures, including those of human and natural systems, is a daunting task
(Thiery et al., 2021a). The same can be said of projecting vulnerability. While there is work on projecting future vulnerability to specific hazards (e.g., Lay et al., 2021), analyses of compound hazards typically do not provide information on vulnerability changes (e.g., Weber et al., 2020), or base vulnerability projections on demographic factors (e.g., Kc et al., 2021). Finally, it is possible to identify elements constraining adaptation action (Theokritoff et al., 2023), yet quantifying future adaptation is complex and coupled with large uncertainties (Moure et al., 2023). We nonetheless argue that the current paper provides a stepping stone to progress in the direction of multi-model impact analyses. In our analysis of compound occurrences, we have further considered specific hazard/impact pairs, but the analysis could be expanded to consider compound occurrences involving more than two hazards/impacts.

Notwithstanding their data and methodological limitations, our results highlight the major implications of global change for the occurrence of future climate-related compound hazards and impacts. In particular, we show a nonlinearity in the increase in compound occurrences for different global warming levels. This underscores the importance of limiting the average global warming to well below 2°C relative to pre-industrial levels, as stipulated in the Paris Agreement (UNFCCC, 2016). In the absence of effective global climate mitigation actions, developing robust adaptation will be crucial to prevent the consequences of a regime shift from a world dominated by individual climate-related hazards and impacts to one where compound occurrences become the norm. The extent of the direct and cascading impacts of climate-related compound occurrences in this new regime are largely unknown. Our findings have significant implications for adaptation planning, as single-hazard approaches may incorrectly estimate risks and vulnerability (Stalhandske et al., 2024). Effective strategies must explicitly account for the compound nature of future hazards and impacts. Furthermore, as larger geographical areas become simultaneously affected, regional resource-sharing mechanisms (such as firefighting resources in Europe or Australia/USA, or post-disaster financial aid systems) will face increasing strain (e.g., Ciullo et al., 2021). Similarly, global trade networks may be affected by more frequent compound occurrences (e.g., Chatsopoulous et al., 2021). Our study ultimately underscores the importance of adopting a holistic perspective on risk that accounts for multiple interacting hazards and impacts—a crucial step toward building climate resilience in a changing world.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

The postprocessed ISIMIP2b data set used in our analysis is available on Zenodo at https://doi.org/10.5281/zenodo.5497633 (Thiery et al., 2021b). The scripts used for the analyses are available through the GitHub repository of the Department of Water and Climate at the Vrije Universiteit Brussel (https://github.com/VUB-HYDR/Mapping_concurrent_hazards_and_impacts-associated_with_climate_extremes) and through Zenodo at https://zenodo.org/records/15095260 (Muheki et al., 2025).

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We acknowledge the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; https://www.isimip.org/, last access: 14 September 2024) and the authors of Lange et al. (2020) and Thiery et al. (2021a, 2021b), whose postprocessed ISIMIP2b data set we have used here. We acknowledge useful discussions with Mariana M. de Brito, which helped to shape this work. We further thank two anonymous Reviewers for their constructive feedback. GM was supported by the European Research Council (ERC) under the European Union's Horizon 2020 and Horizon Europe research and innovation programmes (Grants 948309, “CENÆ” and 10111272, “ICE-MOT”), and by the Swedish Research Council Vetenskapsrådet (Grant 2022-06599). EB was supported from the DFG via the Emmy Noether Programme (Grant 524780515). LSG was supported by the European Union's Horizon Europe research and innovation programme (Marie Sklodowska-Curie Grant 101064940). WT was supported by the European Research Council (ERC) under the European Union's Horizon Europe research and innovation programme (Grant 101076909, “LACRIMA”) and Fonds Wetenschappelijk Onderzoek (Grant 11M8823N).
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