<figure><img src="image_1.png" /></figure>

HAL
open science

# Accounting for exposure in 3D spatiotemporally contiguous heatwaves in Europe 1975-2024

Lou Mandonnet, Aglaé Jézéquel, Fabio d'Andrea, Améline Vallet

► To cite this version:

Lou Mandonnet, Aglaé Jézéquel, Fabio d'Andrea, Améline Vallet. Accounting for exposure in 3D spatiotemporally contiguous heatwaves in Europe 1975-2024. 2026. hal-05495839

HAL Id: hal-05495839

https://hal.science/hal-05495839v1

Preprint submitted on 5 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Accounting for exposure in 3D spatiotemporally
contiguous heatwaves in Europe 1975-2024

L Mandonnet¹,²,³,*, A Jézéquel¹,³, F D'Andrea¹, A Vallet²,⁴

¹ LMD/IPSL, ENS, Université PSL, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris France

² Université Paris-Saclay, AgroParisTech, CNRS, Ecole des Ponts ParisTech, Cirad, EHESS, UMR CIRED, 94130, Nogent-sur-Marne, France.

³ Ecole des Ponts, Marne-la-Vallée, France

⁴ Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique et Evolu-
tion, 91190, Gif-sur-Yvette, France.

* Corresponding author

Corresponding author contact

Corresponding author address : CIRED, 45 avenue de la Belle Gabrielle, 94130, Nogent-sur-Marne, France

Corresponding author e-mail address : lou.mandonnet@lmd.ipsl.fr

ORCID IDs

Lou Mandonnet: 0000-0001-7789-0281

Aglaé Jézéquel: 0000-0002-0957-3126

Améline Vallet: 0000-0002-2731-0098

Fabio D'Andrea: 0000-0002-6249-3168

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lou Mandonnet, Aglaé Jézéquel and Fabio D'Andrea. The first draft of the manuscript was written by Lou Mandonnet and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
## Acknowledgements

The authors acknowledge Yoann Robin for his kind help in the formatting of ERA5 data, as well as Vincent Viguié, Céline Guivarch, Damien Delforge and Davide Faranda for constructive discussions and inputs.

## Data availability

The data created during this study can be accessed at https://doi.org/10.5281/zenodo.18496310

## Code availability

The code is available at https://github.com/loumdt/extreme htws cc3d.

## Abstract

Heatwaves are becoming more common, intense, and widespread as climate change accelerates, increasing their effects on infrastructure, economies, and health. However, heatwaves' material and human effects cannot be adequately captured by their meteorological characteristics alone. In order to address this challenge, we propose a new heatwave index (HWMId_pop), which combines population exposure and meteorological data to provide a more accurate representation of heatwave impacts. Heatwaves are detected using a 3D-event framework, which considers heatwaves as spatiotemporal events that transcend conventional, spatially limited definitions. We validate this new index by comparing it to the impacts of heatwaves recorded in the EM-DAT disaster database for Europe from 1975 to 2021. Our results show that HWMId_pop outperforms traditional heatwave indices by correlating more closely with mortality data and demonstrating a superior ability to identify significant heatwave events. We also examine trends in several commonly used heatwave indices between 1975 and 2024, finding that our proposed index shows a significant increasing trend. We highlight that the increasing affected population, driven by the expanding spatial extent of heatwaves and the rising European population, poses growing health risks. Additionally, we argue that this index can be used as a guide to carry out gap-filling in heatwaves impacts database such as EM-DAT.

**Keywords:** Heatwaves, Extreme climate events, Impact, Exposure, Classification, Observations

# 1 Introduction

Heatwaves are among the most harmful extreme events affecting human populations, mainly due to their severe impacts on health, particularly through increased mortality. For instance, the July 1995 Midwest U.S. heatwave caused at least 700 excess deaths in Chicago (Semenza et al. 1996), while the 2003 Western European heatwave led to 15,000 excess deaths in France (Fouillet et al. 2006). These catastrophic events spurred a surge of research within both the climate and epidemiological communities (Marx et al. 2021). In addition, there is high confidence that heat-related mortality (from both heatwaves and less extreme heat) is increasing rapidly under the influence of climate change (Lüthi et al. 2023).
There is no such thing as a unique way to characterize heatwaves. As Perkins (2015) noted, “it seems that almost, if not every climatological study that looks at heatwaves uses a different metric”. Indeed, the characterization of a heatwave is dependent on the goal of the study. Broadly, the literature can be divided into two main methodological approaches.

The first approach uses time-series analyses to explore the relationship between heat stress and mortality, quantifying the relative risk associated with meteorological conditions. Extensive empirical evidence shows that the temperature–mortality relationship is nonlinear and depends on multiple factors, including location, age, sex, race, and socio-economic conditions (e.g. Curriero (2002), Basu (2002), Anderson and Bell (2009), Lo et al. (2023), Scovronick et al. (2024)). Such studies typically focus either on individual locations (Basu 2002) or on multiple sites through multivariate meta-analytical frameworks that estimate location-specific temperature–mortality relationships (Gasparrini et al. (2015), Ma et al. (2015), Zhao et al. (2021), Vicedo-Cabrera et al. (2021)).

The second approach relies on thresholds of temperature intensity and duration to identify heatwaves, often through the use of heatwave indices. These indices can be applied at different spatial scales. At the local level, they facilitate fine-scale analyses of exposure and vulnerability (Mashhoodi and Kasraian (2024), Cheng and Sha (2024)). At regional or global scales, they are used to study the occurrence, intensity (Sillmann et al. (2013), Russo et al. (2014), Russo et al. (2015)) and exposure to heatwaves (Chambers (2020), Freychet et al. (2022)). As an alternative to this approach of spatial scales, Luo et al. conceptualized heatwaves as three-dimensional (3D) climate events, defined at national or regional scales, and developed indices to characterize them (Luo et al. (2022a), Luo et al. (2022b)). This framework aims to describe heatwaves as coherent spatiotemporal phenomena, rather than as fragmented events constrained by arbitrary administrative boundaries.

Neither time-series analyses nor local-scale threshold approaches adequately capture the full scope of impacts generated by a single heatwave affecting multiple administrative units, as heatwaves often span thousands of square kilometers. This is better captured by the 3D-event characterization framework, which nevertheless fails to account for exposure or vulnerability (unlike time-series and local-scale threshold approaches).

Furthermore, existing indices have rarely been validated using empirical impact data, despite their stated purpose of reflecting human impacts. Few studies have compared meteorological indices with observed impact data — either through modeling (van der Wiel et al. 2020) or empirical observations (Tschumi and Zscheischler 2020). To our knowledge, none has focused on heatwaves. This reveals a key knowledge gap between studies demonstrating the importance of exposure in the understanding of heatwave impacts and the indices employed in 3D-event analyses.

In this paper, we propose a new heatwave index designed for 3D-event characterization, which explicitly combines temperature and population density. This index is intended to capture both heat intensity and human exposure, thereby providing a more realistic representation of the potential human impacts of a heatwave. Using the 3D-event framework, we identify heatwaves in Europe between 1975 and 2024 from
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Overview of the methodology. CC3D and ROCC respectively stand for Connected Components 3D and Receiver Operating Characteristics Curve. The heatwaves indices are described in Table 1. A sensitivity analysis is carried out to test the effects of the choice of the temperature variable and the thresholds.</figcaption></figure>

ERA5 reanalysis data (Hersbach et al. 2020) through a connected-components algorithm and compare our index to other indices from the literature. We rely on the Emergency Events Database (EM-DAT), which compiles disaster records and their impacts since 1900 (Delforge et al. 2025), to compare our catalog of events, as well as our index, with observed impact data at the event scale. We proceed to analyze heatwaves trends in Europe for different indices from 1975 to 2024. Finally, we demonstrate how our index can be used to guide literature-based searches for evidence of impacts from heatwaves not recorded in EM-DAT.

## 2 Data and Methods

In this study, we build upon existing approaches that use the connected-component-3D (CC3D) algorithm for identifying heatwaves. We also introduce a new index, HWMId_pop, that explicitly incorporates human exposure, offering a more comprehensive characterization of heatwaves. Our methodology is composed of four key steps (Figure 1), which include: (1) the definition of thresholds on duration and temperature intensity for identifying heatwaves at the grid point level; (2) the detection of heatwave events with the help of a CC3D algorithm; (3) the computation of the relevant heatwaves indices, and (4) the validation with observed impact data.
## 2.1 Data sets

## 2.2 The ERA5 meteorological dataset

In order to identify heatwaves, we rely on meteorological data from the ERA5 reanalysis. The ERA5 dataset, produced by ECMWF, is a global hourly reanalysis dataset, at a nominal 0.25° resolution (Hersbach et al. 2020). Hourly data are used to obtain daily maximum temperature. The data is restrained to 34°N-71.5°N x 12.5°W-45°E, corresponding to continental Europe, and to the period 1975-2024. Since GHS-POP is restricted to after 1975, we also restrict the other datasets to after 1975. Since EM-DAT is restricted to before 2021, ERA5 is also restricted to 1975-2021 whenever we compare with EM-DAT data.

## 2.3 The GHS-POP demographic dataset

We use the GHS-POP demographic dataset as a source of data for population count, which is available every 5 years, between 1975 and 2024 (Schiavina et al. 2019). We derive yearly estimates through linear interpolation (Lindersson et al. (2023), Sauer et al. (2024)). The dataset, originally at a 30" spatial resolution, is regridded to match the ERA5 grid for compatibility in our analysis.

## 2.4 The EM-DAT impact database

We use the international disaster database EM-DAT, produced by the Centre for Research on the Epidemiology of Disasters (CRED; Delforge et al. (2025)).

EM-DAT records disasters and their impacts from 1900 to present day. The impacts data are collected from UN agencies, non-governmental organizations, and insurance companies. Events recorded in EM-DAT fulfill at least one of the following criteria:

1. 10 people or more died because of the event,

2. 100 people or more were affected (which does not include deaths), injured, or homeless,

3. The impacted country declared an emergency state or called for international aid in the aftermath of the event.

Although the EM-DAT database is known to be prone to several types of biases, including specific issues for heat-related events such as heatwaves (Lin et al. (2021), Brimicombe et al. (2021a), Jones et al. (2023), Delforge et al. (2025)), it remains the most comprehensive global source of historical heatwaves data and continues to be widely used in physical sciences, including in recent heatwave studies (Harrington and Otto (2020), Tschumi and Zscheischler (2020), Mithal et al. (2024), Quilcaille et al. (2025), Delforge et al. (2025)). Moreover, reporting biases are less pronounced in Europe compared to other regions (Harrington and Otto 2020), and restricting the analysis to events up to 2021 avoids recent classification issues that arose from 2022 onward, where some temperature-related fatalities were recorded as heatwaves even when occurring outside recognized heatwave periods (Delforge et al. 2025).
In EM-DAT, events that impact multiple countries are recorded separately for each affected country. In our study, we focus on the 88 heatwaves recorded in EM-DAT in June, July and August between 1975 and 2021 within our area of interest: Europe (according to EM-DAT geographical classification), Turkey, Azerbaijan, Georgia, and Armenia. The first recorded heatwave occurred in 1985, and only 12 recorded heatwaves occurred before 2000, out of the 88 recorded heatwaves considered here. According to the EM-DAT geographical classification, among the 88 recorded heatwaves, 32 occurred in Southern Europe, 31 in Western Europe, 16 in Eastern Europe, 7 in Northern Europe, and 2 in Turkey (considered Western Asia by EM-DAT). These 88 country-level heatwaves correspond to 35 continental-level heatwaves.

We use the number of deaths, "Total Deaths", as the way to quantify the heatwaves' impacts since it is a key impact of heatwaves in the literature and the best documented in EM-DAT, with 77 of the 88 heatwaves having "Total Deaths" values (Delforge et al. 2025).

## 2.5 Identifying and characterizing heatwaves

Heatwaves are typically defined by thresholds on intensity and a minimum duration. Regarding intensity, relative thresholds are often used in the literature e.g. to identify heatwaves (Stefanon et al. (2012), Sillmann et al. (2013), Russo et al. (2015), Schär et al. (2016), Luo et al. (2022a), Luo et al. (2022b)). They reflect how unusual the temperature is compared to the climatology of the location of interest. In order to identify heatwaves as 3D events, we first find hot grid points and then look for spatio-temporally contiguous hot grid points.

### 2.5.1 Setting thresholds by gridpoint (Step 1)

We define a heatwave as the ensemble of contiguous grid points where the daily maximum temperature exceeds the daily threshold for at least 4 consecutive days (Stefanon et al. 2012). That is, if a grid point is not in a sequence of at least 4 days of the same location exceeding the daily threshold, it is not considered as part of a heatwave.

We define the daily threshold for every gridpoint on day $d$ as the 95th percentile of daily maximum temperature, centered on a 15 day window, i.e. the 95th percentile of the following $A_d$ distribution:

$$A_d = \bigcup_{y=1975}^{2024} \bigcup_{i=d-7}^{d+7} T_{y,i} \qquad (1)$$

where $T_{y,i}$ is the daily maximum temperature of the day $i$ of year $y$ at the given location (Russo et al. (2015), Luo et al. (2022a)). The 15-days size of the temporal window is chosen considering that a too low amount of data entails a bias (Zhang et al. 2011).

Then, we carry out the heatwave detection process per se, for the summer months June, July and August.
### 2.5.2 Identifying heatwaves as spatio-temporal events (Step 2)

We identify spatiotemporally contiguous heatwaves by analyzing gridded daily temperature data using a 3D Connected Component Labeling (CC3D) algorithm, available through the Python library connected-components-3d (Silversmith 2021).

While CC3D has been applied in medical imaging (O'Reilly et al. (2021), Park et al. (2021)), it also proved useful in detecting climatological events such as heatwaves (Vogel et al. (2020), Reddy et al. (2022), Luo et al. (2022a), Luo et al. (2022b)), but also droughts (Lloyd-Hughes (2012)) or gross primary production extremes (Zscheischler et al. (2013), Zscheischler et al. (2014b), Zscheischler et al. (2014a)). In this study, we use the CC3D algorithm in a similar way to Luo et al. (2022a). This method allows us to treat heatwaves as spatio-temporal events, extending beyond the limitations of traditional spatially or temporally constrained heatwave definitions.

For each summer (June to August), we transform the temperature values stored in a 3D voxel array (time × latitude × longitude) into a binary 3D array of 0 and 1. A voxel (short for volumetric pixel) marked as "1" represents a hot day, i.e. a voxel exceeding the intensity and duration thresholds defined in Step 1, while "0" indicates otherwise.

This binary array is then put into the CC3D algorithm to identify connected voxel clusters, under the assumption of 26-connectivity. These clusters correspond to spatiotemporally continuous heatwaves. The 26-connectivity accounts for all neighboring grid cells and time steps within a 3 × 3 × 3 cube: 9 grid cells from the prior day, 8 surrounding the central cell on the current day, and 9 from the subsequent day. This approach groups all 26-connected "1" voxels into a 3D representation of a single heatwave event.

We use the "dust" function of the connected-components-3d Python module (Silversmith 2021) to carry out the filtering of hot grid points identified at step 1, before applying the CC3D algorithm. As a lot of the grid points identified at step 1 correspond to heatwaves of small spatial extension (i.e. approximately 80% of detected heatwaves are less than 150'000 km², a bit more than the area of Greece), this filtering is necessary to avoid noise. To find the correct threshold for the filtering, we implement the CC3D algorithm using an increment of 25 voxels and look for a stable number of identified heatwaves. That is, we stop the increment when the number of identified heatwaves decreases by less than 1% between two iterations.

### 2.5.3 Heatwaves characterization (Step 3)

After identifying heatwaves using the CC3D algorithm, we compute four indices to characterize their severity and human impact. Our key innovation is the HWMId_pop index, which extends traditional heatwave indices by incorporating human exposure. Specifically, this index weights the HWMId value (Russo et al. 2015) of each grid point by its population density, derived from the GHS-POP dataset, and sums them. This allows us to assess the potential human impact based on both heatwave intensity and exposure.

In addition to HWMId_pop, we compute three other indices: Intensity, HWMId_sum, and Affected Population. The Intensity index is a standard measure
<table><caption>**Table 1** List of the four heatwaves indices and their definition.</caption><thead><tr><td>Index</td><td>Definition</td></tr></thead><tbody><tr><td>Intensity</td><td>Area-weighted average of the temperature exceedance relative to the threshold over the whole event</td></tr><tr><td>HWMId_sum</td><td>Spatial sum of an index similar to the HWMId defined in Russo et al. (2015). HWMId_sum is the sum of the magnitudes of the consecutive days composing the heatwave, with daily magnitude M<sub>d</sub> calculated as follow: M<sub>d</sub> = (T<sub>d</sub> - T<sub>25</sub>) / (T<sub>75</sub> - T<sub>25</sub>) if T<sub>d</sub> ≥ T<sub>25</sub> and M<sub>d</sub> = 0 elsewhere ; T<sub>d</sub> is the daily maximum temperature on day d, T<sub>25</sub> and T<sub>75</sub> are, respectively, the 25th and 75th percentile values of the time series composed of 47 year annual Tmax within the reference period 1975–2021.</td></tr><tr><td>Affected population</td><td>The population affected by the heatwave, calculated by summing the population over the affected grid points. Even if a pixel is affected several days, it is only counted once.</td></tr><tr><td>HWMId_pop</td><td>A variant of HWMId_sum, where each grid point is weighted by its population density before summing (derived from GHS-POP).</td></tr></tbody></table>

of heatwaves (e.g. Luo et al. (2022a) and Luo et al. (2022b)). We derive HWMId, originally used for 2D heatwave mapping, from Russo et al. (2015). HWMId_sum is an extension of HWMId suited for 1D characterization of 3D heatwaves, by summing it spatially, but it does not account for exposure. The Affected Population index quantifies the impact of heatwaves on the population independently of temperature. Comparing HWMId_pop to HWMId_sum and Affected Population allows us to demonstrate its better performance by incorporating both heatwave intensity and human exposure. The 4 indices and their definitions are presented in Table 1.

### 2.5.4 Analyzing the overlap between meteorological heatwaves and documented impacts (Step 4)

To assess the validity of our indices, we compare the heatwaves identified by the CC3D algorithm (referred to as "CC3D heatwaves") with those documented in the EM-DAT database (referred to as "EM-DAT heatwaves"). We analyze the overlap between the 88 country-level heatwaves recorded in EM-DAT, and the outcomes of the CC3D algorithm to identify "overlap heatwaves", i.e. the CC3D heatwaves that match EM-DAT heatwaves.

The objectives of this step are (i) to identify CC3D heatwaves that match documented EM-DAT impacts (i.e. overlap heatwaves), and (ii) to attribute a quantitative impact (number of deaths) to these overlap heatwaves.

Since heatwave impacts may occur after the heatwave ends (e.g., due to delayed mortality), we extend the temporal bounds of EM-DAT heatwaves by three days to account for potential lags in impact reporting (e.g. Fouillet et al. (2006), Corobov et al. (2013)). However, a significant number of heatwaves recorded in EM-DAT do not have clearly defined temporal bounds (e.g. only a month is indicated for the start or end date). In this case, we use the full month to analyze overlap with CC3D heatwaves. If this temporal inaccuracy leads to the EM-DAT heatwave overlapping with several CC3D heatwaves, we either look for evidence in the scientific literature and newspapers
<table><caption>**Table 2** EM-DAT heatwaves recorded in between 2010 and 2015 in our area of interest. The days in parenthesis, not included in EM-DAT, correspond to the day that we consider for the overlap analysis, according to the 3-days window of potential impact lag, and our choice regarding missing days. According to our methodology, the impact of the 2010 Russian heatwave is distributed to CC3D heatwaves A, B and C. The impact attributed to the CC3D heatwave E is the sum of the impacts in Belgium and France.</caption><thead><tr><th>Country</th><th>Year</th><th>Start Month</th><th>Start Day</th><th>End Month</th><th>End Day</th><th>Total Deaths</th><th>CC3D overlap</th></tr></thead><tbody><tr><td>Russia</td><td>2010</td><td>6</td><td>(1)</td><td>8</td><td>(31)</td><td>55736</td><td>A, B, C</td></tr><tr><td>Italy</td><td>2011</td><td>8</td><td>17 (14)</td><td>8</td><td>24 (27)</td><td>10</td><td>D</td></tr><tr><td>United Kingdom</td><td>2013</td><td>7</td><td>(1)</td><td>7</td><td>(31)</td><td>760</td><td>None</td></tr><tr><td>Belgium</td><td>2015</td><td>6</td><td>30 (27)</td><td>7</td><td>5 (8)</td><td>410</td><td>E</td></tr><tr><td>France</td><td>2015</td><td>6</td><td>29 (26)</td><td>8</td><td>9 (12)</td><td>3275</td><td>E</td></tr></tbody></table>

to further refine EM-DAT with more precise temporal bounds (1 case) or we distribute the impact proportionally to the cumulative area affected by each heatwave (7 cases).

Since EM-DAT impacts are recorded at the national level (Delforge et al. 2025), we use country-level information to analyze the spatial overlap between CC3D heatwaves and EM-DAT heatwaves. We do not use sub-national location information recorded in EM-DAT. For each CC3D heatwave, we look for matching EM-DAT impacts in the affected countries, at the corresponding time. Even if a country is only affected on 10% of its area by a CC3D heatwave, we consider it a match with the entire corresponding EM-DAT heatwave if the dates also match.

As shown in Table 2, a single CC3D heatwave can overlap with several EM-DAT events. In this case, the resulting impact attributed to the CC3D heatwave is the sum of the impacts of the corresponding EM-DAT heatwaves.

## Indices performance evaluation

The performance of each heatwave index is evaluated using two scores:

* Area Under the Receiver Operating Characteristic Curve (AUROCC, Fawcett (2006)): this score evaluates the ability of the index to classify heatwaves correctly i.e. to assign the highest index values to the overlap heatwaves.

* Pearson Correlation Coefficient: this score evaluates the relationship between the index values and the number of deaths, providing insight into how well each index reflects the actual impact of heatwaves. Since an impact value is needed, this score is only computed for overlap heatwaves.

## Methodology summary

First, thresholds are set on daily maximum temperature at the grid point level on intensity (95th percentile of $A_d$ distribution) and duration (4 days). Then we identify heatwaves with the CC3D algorithm. We then compute values of the four indices for all heatwaves. Finally, we find the EM-DAT impacts matching the CC3D heatwaves and compare the indices ability to reflect human impacts.
## 2.6 Sensitivity analysis

A sensitivity analysis is carried out to test the effects of the choice of the temperature variable and the duration and intensity thresholds. The following parameters are tested, resulting in 12 combinations:

* 2m-temperature: daily maximum, daily average, daily minimum

* duration threshold: 4 days, 3 days

* intensity threshold: 95^th^ percentile, 90^th^ percentile of A_d distribution

## 2.7 Heatwaves evolution

In addition to the four indices presented in Table 1, we evaluate the trends for 4 other heatwave indices: Duration (in days), Maximum of the threshold exceedance (hereafter called Max, in °C), Spatial extent (in km²), and another measure of the Affected population. This index, called Relative affected population, corresponds to the affected population computed as the percentage of the European population of the year of occurrence of the heatwave. It therefore does not depend on the temporal evolution of the population.

There is no recent comprehensive study of heatwave characteristics at the continental level for Europe. Indeed, to our knowledge, the last of such studies is Russo et al. (2015). Hence, to specifically assess the contribution of the last 10 years of the 1975-2024 period in the overall trends, and to check for a potential acceleration of these trends, we compare trends for the 1975-2014 periods and 1975-2024 periods.

For each index and both periods, we compute a linear regression of the index along time and show the slope of the regression model, β. We also assess the variation of β between 1975-2014 and 1975-2024 when the trend is significant for both periods.

## 2.8 Investigating non-matched CC3D heatwaves

Given that EM-DAT is known to be incomplete, we investigate whether high-scoring CC3D heatwaves that do not overlap with documented EM-DAT heatwaves correspond to missed or underreported events. For the 10 highest-scoring non-overlapping heatwaves, we search the traces of possible impacts in the scientific literature and press archives, in order to verify whether impacts were documented but not recorded in EM-DAT, potentially filling gaps in the database.

# 3 Results

## 3.1 Heatwaves catalog and comparison with EM-DAT

We identify 218 heatwaves between 1975 and 2024 using the CC3D method, with 199 heatwaves occurring during the 1975-2021 period. Among these, 31 heatwaves overlap with 63 of the 88 heatwaves recorded in the EM-DAT database.

The new heatwave index, HWMId_pop, which combines meteorological intensity with population exposure, performs better than the other indices, both in terms of correlation with observed mortality data and in its area under the receiver operating characteristic curve (AUROCC). Specifically, HWMId_pop not only better ranks
<table><caption>**Table 3** Performance of the 4 indices. The best performances are highlighted in blue, and the worst ones in red. The stars in the "Correlation coefficient" column are an indication of the p-value: * if *p* < 0.05, ** if *p* < 0.01 and *** if *p* < 0.001.</caption><thead><tr><td>Index</td><td>Correlation coefficient</td><td>AUROCC</td></tr></thead><tbody><tr><td>Intensity</td><td>0.353117</td><td>0.627811</td></tr><tr><td>HWMId_sum</td><td>0.676989***</td><td>0.711243</td></tr><tr><td>Affected population</td><td>0.624142***</td><td>0.727613</td></tr><tr><td>HWMId_pop</td><td>0.81214***</td><td>0.777909</td></tr></tbody></table>

the overlap heatwaves by their mortality impact, but it also accurately associated the most impactful heatwaves with the highest values in the distribution, outperforming the traditional Intensity (which is frequently used in the literature, e.g. Guigma et al. (2020), Luo et al. (2022a), Luo et al. (2022b)) index, which showed notable inconsistencies with the mortality data (see Table 3 and Figure 2). It is also noteworthy that HWMId_pop performs better than both its meteorological (HWMId_sum) and exposure (Affected population) contributions when taken independently.

This result is robust to different thresholds and variables selection. Our index, HWMId_pop, performs best in the 12 combinations tested in a sensitivity analysis regarding the pearson correlation, and performs best in 8 out of 12 combinations regarding AUROCC (SM1).

## 3.2 Evolution of European heatwaves 1975-2024

We compute trends for both the 1975-2014 and the 1975-2024 periods, with significant trends for 4 indices for the former period and 8 indices for the latter. Notably, HWMId_pop increases by 37.76% per decade (Table 4). Both components of the index, HWMId_sum and Affected Population, also exhibit significant increases between 1975 and 2024. Specifically, the number of people exposed to heatwaves has grown by approximately 11 million per decade, and the proportion of the European population affected has increased by 1.32 percentage points per decade. Meanwhile, HWMId_sum has risen by 35.14% of the typical 1975-2014 event per decade. Among all indices, the Intensity index is the only one that does not show a significant trend in either period. While four indices—Max, Duration, HWMId_sum, and HWMId_pop—display significant upward trends only for the 1975-2024 period, Frequency, Spatial extent, Affected population and Relative affected population increase significantly in both the 1975-2014 and 1975-2024 periods, with a recent acceleration, as shown by the increase in the value of $\beta$ for the 1975-2024 period relative to the 1975-2014 period.

For 1975-2024, the Frequency trend ($\beta$ = 1.03 events per decade) implies that each decade has brought, on average, one additional heatwave per summer, amounting to five additional events between 1975 and 2024. The spatial extent of heatwaves has likewise expanded, with 1.32% more of the European land area affected per decade. This increase is partly responsible of the growth of the Affected population and Relative affected population indices. Finally, the Duration trend ($\beta$ = 1.59 days per decade) indicates that typical heatwaves lengthened by approximately 1.6 days per decade over the study period.
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Distribution of the 199 heatwaves detected by CC3D between 1975 and 2021 for the four indices. The blue curve represents the Kernel Density Estimation (KDE) of the values of the indices. The blue bars at the bottom correspond to the actual points used to build the KDE curve. The colored bars represent the 31 overlap heatwaves (i.e. the CC3D heatwaves that match EM-DAT heatwaves) while the color gives the impact (the number of deaths), on a logarithmic color scale.</figcaption></figure>

The trends in Table 4 are illustrated in Figure 3. In this figure, the trends in total number, spatial extension, duration and HWMId_pop are represented at the same time.

## 3.3 Unmatched events: EM-DAT and CC3D discrepancy

### 3.3.1 EM-DAT heatwaves not detected by CC3D

Among the 26 EM-DAT heatwaves that are not detected with the CC3D algorithm, 8 heatwaves caused more than 100 deaths. By merging them thanks to their EM-DAT identifier (i.e. getting rid of the country-level cutting of EM-DAT), the number of undetected heatwaves decreases to 16, including 4 that caused more than 100 deaths. Undetected heatwaves are mainly located in United Kingdom, Romania and Italy respectively (4, 3 and 3 occurrences). The undetected EM-DAT heatwaves are shown in Figure 4. The number of these heatwaves depend on the choice of the temperature variable and on the chosen thresholds (see SM1). In particular, setting a less restrictive threshold either on duration or intensity leads to a lower number of undetected EM-DAT heatwaves. Moreover, all other things being equal, choosing the daily minimum
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Scatterplot of the 218 heatwaves detected with the CC3D algorithm between 1975 and 2024. The X axis represents the Year of occurrence of the heatwaves, and the Y axis represents the spatial extent (in km²). The size of the dots represent the duration (in days), while the color represents the value of the index HWMId_pop, with a logarithmic color scale. The overlap heatwaves are identified by triangle markers, whereas the round markers correspond to the CC3D heatwaves that do not overlap with EM-DAT heatwaves. The EM-DAT data was restricted to 1975-2021, so there is no overlap heatwaves after 2021. The major 2003 and Russian 2010 heatwaves are highlighted. The Russian 2010 heatwave is composed of 3 CC3D heatwaves.</figcaption></figure>

of the temperature systematically leads to a higher number of undetected EM-DAT heatwaves.

### 3.3.2 Top 10 CC3D heatwaves unmatched in EM-DAT

All 10 CC3D heatwaves that are not documented in EM-DAT with the highest index values (according to the HWMId_pop index) are mentioned in the scientific literature or in the national and regional press, with mentions of dead people for 8 of them (see Table 5 and references therein). The column "Deaths" of Table 5 informs on the presence of quantitative death records in the associated references. These death records are not homogeneous and do not allow to assign a rigorous value. For instance, regarding the heatwave identified in 1976, Rey et al. (2007) indicate that "The excess mortality during the HW-associated mortality period was about [...] 5100 deaths" in France, while Ellis et al. (1980) clearly identify an increase in mortality in Birmingham without giving a clear number: "The average number of daily deaths [...] increased
<table><caption>**Table 4** Heatwaves indices trends for the 1975-2014 and 1975-2024 periods. $\beta$ is the slope of the linear regression of the index, i.e. the index trend. The stars in the "$\beta$" column are an indication of the p-value, i.e. the significance of the trend: * if $p < 0.05$, ** if $p < 0.01$ and *** if $p < 0.001$. The units of HWMId_sum and HWMId_pop are a percentage of the mean value of the considered index for the 1975-2014 period. The variation rate is computed relatively to the 1975-2014 $\beta$ value. It is only showed for trends that are significant in both periods. The "Relative affected population" is computed as a percentage of the European population of the year of the heatwave.</caption><thead><tr><th rowspan="2">Index</th><th rowspan="2">Unit</th><th colspan="3">Period</th></tr><tr><th>1975-2014</th><th>1975-2024</th><th>Variation rate</th></tr><tr><th></th><th></th><th>β</th><th>β</th><th>% of β</th></tr></thead><tbody><tr><td>Frequency</td><td>Events/decade</td><td>0.96***</td><td>1.03***</td><td rowspan="3">7.29</td></tr><tr><td>Intensity</td><td>°C/decade</td><td>-0.02</td><td>0.04</td></tr><tr><td>Max</td><td>°C/decade</td><td>0.13</td><td>0.21**</td></tr><tr><td>Spatial extent</td><td>% of Europe area/decade</td><td>1.3**</td><td>1.32***</td><td rowspan="3">1.54</td></tr><tr><td>Duration</td><td>Days/decade</td><td>0.72</td><td>1.59***</td></tr><tr><td>HWMId_sum</td><td>% of typical event/decade</td><td>45.28</td><td>35.14*</td></tr><tr><td>Affected population</td><td>10<sup>6</sup> ppl/decade</td><td>8.53*</td><td>11.24***</td><td>31.77</td></tr><tr><td>Relative affected population</td><td>% of European population/decade</td><td>0.99*</td><td>1.32***</td><td>33.33</td></tr><tr><td>HWMId_pop</td><td>% of typical event/decade</td><td>39.04</td><td>37.76*</td><td></td></tr></tbody></table>

significantly by nearly 20 per cent during this hot fortnight and by over 30 per cent from 3 to 5 July". More details regarding the available death records are given in Supplementary Materials (see SM2).

This could eventually serve to fill the gaps in the EM-DAT database.

# 4 Discussion and Conclusion

## 4.1 Advancing the 3D-Event Framework

This study situates the analysis of heatwave impacts within the 3D-event framework, a powerful approach for understanding extreme weather events by considering their spatial and temporal dimensions. By adopting this framework, we are able to go beyond traditional heatwave identification, which often focus only on intensity and duration, and we propose a more comprehensive index, HWMId pop, which incorporates both meteorological intensity and human exposure. This new index performs better than existing indices regarding its ability to reflect human impacts.

The 3D-event framework applied to heatwaves, as used by Vogel et al. (2020) and Luo et al. (2022a), conceptualizes heatwaves not just as isolated events but as spatially and temporally contiguous phenomena. This perspective is critical for heatwaves, as they can span vast regions and last several days, affecting millions of people. By treating heatwaves as 3D events, we are able to capture their full extent, and to better compare them with EM-DAT records. The HWMId pop index we propose here, which combines temperature data with population density, is a novel extension of the 3D-event framework. Our findings confirm that this index correlates better with observed mortality data than other indices (Figure 2 and Table 3), highlighting the importance
<figure><img src="image_6.png" /><figcaption>**Fig. 4** List of the EM-DAT heatwaves not detected by CC3D, sorted by Total Deaths. Table a displays the 16 undetected EM-DAT heatwaves grouped by EM-DAT heatwave identifier, and the corresponding deaths and affected countries (numbers in parenthesis correspond to the deaths in each country). These 16 heatwaves correspond to 26 country-level heatwaves. Maps b, c and d correspond to the most affected country of each of the three most lethal heatwaves, indicated with bold letters in panel a. The plotted variable is the average of the daily maximum temperature anomaly over the heatwave period estimated from EM-DAT. Map b displays the whole month of July of 2003 for Switzerland. Map c displays the whole 2020 summer for Great Britain. Map d displays the whole month of July of 2013. Country borders are highlighted in green.</figcaption></figure>

of integrating exposure into the 3D-event approach—a critical research gap that has yet to be fully addressed.

## 4.2 Trends under the influence of climate change

The significant increasing trend of our index between 1975 and 2024 is closely linked to both climate change and demographic shifts (Table 4). As global temperatures rise, the frequency and severity of heatwaves are projected to worsen, further exacerbating the public health risks associated with extreme heat. Our results show that the number of people affected by heatwaves has increased significantly over the past decades, with an additional 11 million people affected by heatwaves each decade, a trend driven by
both the growing spatial extent of heatwaves and the increasing European population. Globally and in the European region, the increasing trend in heatwaves frequency, spatial extent and duration is a well-known result, and the strengthening of these trends during the last 10 years was predictable under the influence of human-induced climate change (Russo et al. (2015), Zschenderlein et al. (2019), Vogel et al. (2020), Seneviratne et al. (2021), Rousi et al. (2022), Lhotka and Kyselý (2022)). The observed increasing trend of exposure to heatwaves is also consistent with previous studies from the literature (Chambers (2020), Freychet et al. (2022)). In contrast, we detect no trend regarding the evolution of the Intensity index, which is surprising and contradicts with literature (Seneviratne et al. (2021), Rousi et al. (2022)).

On the other hand, our findings regarding the undetected EM-DAT events shows a predominance of Eastern European countries in Figure 4a, aligning with previous research (e.g. Tschumi and Zscheischler (2020) and van der Wiel et al. (2020)), which demonstrate that developed countries need stronger climate anomalies to experience significant health impacts, highlighting the role of adaptation measures in mitigating heatwave mortality. However, among the most impactful undetected EM-DAT heatwaves stand 4 occurrences of United Kingdom heatwaves. This result might be explained by the flaws of the United Kingdom early warning system, namely the fact that resources are not allocated towards the most vulnerable people, as identified by Brimicombe et al. (2021b) or Carvalho and Spataru (2023).

## 4.3 Limitations and areas for future research

The increasing vulnerability of populations, particularly the elderly, further underscores the need for integrated approaches to assessing heatwave risks. Vulnerability is not determined by the intensity of the heatwave but by socio-economic factors such as age, gender, income levels, access to healthcare, housing quality, or even access to air conditioning. While we did not account for vulnerability at any point in our study, this concept is key in understanding some of our results (Singer et al. (2016), Yang et al. (2019), Simpson et al. (2021), Boni et al. (2023)). It is also an essential dimension of the design of efficient adaptation policies and early warning systems. For instance, the French national strategy "Plan Canicule" incorporates daily meteorological information for the whole duration of the summer season, in addition to a high number of vulnerability factors in relation to the health of the population (6 variables), and their living conditions (9 variables), that offer much more detail than combining meteorological and population density alone.

In addition to the uncertainty coming from the two other datasets, GHS-POP and ERA5, this study suffers several limitations deriving from EM-DAT, as explained throughout the paper. The definition of our new index, HWMId_pop, is based on a rich body of literature showing the importance of exposure in the understanding of heatwaves impact. We use EM-DAT as a way of validating the performance of this new index against observational data, although with several limitations.

First, the impact data is sparsely filled, as pointed out by Jones et al. (2022) and Jones et al. (2023): 11 out of the 88 heatwaves lack data for the number of deaths, while the sample size is already small and challenges the robustness of our results.
Moreover, the absence of consistent and systematic geocoding of impact data in EM-DAT prevents a finer spatial analysis of the overlap between the CC3D heatwaves and EM-DAT. The absence of precise temporal boundaries in EM-DAT also challenges the temporal analysis, even though external scientific sources often converge to more precise temporal bounds.

Finally, we show that several heatwaves events are simply missing from the EM-DAT dataset (Table 5). EM-DAT is known to be incomplete before 2000, as shown in Lin et al. (2021) and Delforge et al. (2025). The problem is double: (1) events are missing and (2) there are discrepancies in the ways of reporting events before and after 2000, with several heatwaves events being reported as droughts before 2000. As an illustration of this point, the oldest heatwaves of the EM-DAT dataset within our period and area of interest occurred in 1985. Nevertheless, we also find several heatwaves missing in EM-DAT after 2000.

More importantly, the analysis of top scoring heatwaves missing in EM-DAT opens the way for a systematic gap-filling of EM-DAT, by identifying spatio-temporal boundaries that can be used for systematic searching in gray and scientific literature. This systematic work, beyond the scope of this study, could be carried out by future research using natural language processing. Indeed, recent advances in natural language processes have enabled the creation of new impact databases for a range of event including droughts (Sodoge et al. 2024), floods (Madruga De Brito et al. 2025), heatwaves (Cecinati et al. 2019) and storms (Liu et al. 2022) based on diverse text sources such as newspapers, social media or Wikipedia (Madruga De Brito et al. (2020), Zhang et al. (2022), Li et al. (2025)). Our work contributes to identifying heatwaves from 1975 to the present day, which could be used to supervise text-mining and either create heatwave impact databases or complete existing multi-hazard databases such as EM-DAT.

Future studies could also explore how the HWMId_pop index could be adapted for other regions or scaled to future climate scenarios, offering insights into how heat-wave risks might evolve under different climate change and demographics scenarios. Indeed, the framework used here is suited to be applied to climate model outputs, in combination to population projection datasets under different population growth scenarios. This combination would allow to evaluate uncertainty over both meteorological intensity and population changes.

Additionally, the integration of socio-economic vulnerability data, such as age, gender or income levels, would provide a more nuanced understanding of the factors that mediate heatwave impacts, leading to the potential creation of another heatwave index adapted to the 3D-event framework.

## 4.4 Conclusion

This study provides a new methodology for assessing heatwave impacts by placing the analysis within a 3D-event framework. The HWMId_pop index, which integrates both climate and population data, offers a more accurate measure of the human impacts of heatwaves. As climate change continues to exacerbate the severity and extent of heatwaves, incorporating demographic data into heatwave indices will be crucial to better align climatological studies of heatwaves with impact research.
Future research should focus on consolidating heatwave impact data, and studying
the combined contributions of population evolution and climate change for projecting
heatwave risks under various scenarios in a 3D-event framework. In doing so, we can
better understand the growing threat posed by extreme heat events.

5 Data availability

The ERA5 dataset is available at the url https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels.

The EM-DAT dataset is accessible at https://public.emdat.be/. Last accessed:
2025/10/10

GHS-POP dataset is accessible at
https://human-settlement.emergency.copernicus.eu/download.php?ds=pop
## References

Anderson BG, Bell ML (2009) Weather-Related Mortality: How Heat, Cold, and Heat Waves Affect Mortality in the United States. Epidemiology 20(2):205-213.
https://doi.org/10.1097/EDE.0b013e318190ee08, URL https://journals.lww.com/00001648-200903000-00011

Basu R (2002) Relation between Elevated Ambient Temperature and Mortality: A Review of the Epidemiologic Evidence. Epidemiologic Reviews 24(2):190-202.
https://doi.org/10.1093/epirev/mxf007, URL https://academic.oup.com/epirev/article-lookup/doi/10.1093/epirev/mxf007

Boni Z, Bieńkowska Z, Chwałczyk F, et al (2023) What is a heat(wave)? An interdisciplinary perspective. Climatic Change 176(9):129. https://doi.org/10.1007/s10584-023-03592-3, URL https://link.springer.com/10.1007/s10584-023-03592-3

Brimicombe C, Di Napoli C, Cornforth R, et al (2021a) Borderless Heat Hazards With Bordered Impacts. Earth's Future 9(9):e2021EF002064. https://doi.org/10.1029/2021EF002064, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021EF002064

Brimicombe C, Porter JJ, Di Napoli C, et al (2021b) Heatwaves: An invisible risk in UK policy and research. Environmental Science & Policy 116:1-7. https://doi.org/10.1016/j.envsci.2020.10.021, URL https://linkinghub.elsevier.com/retrieve/pii/S1462901120313782

Can G, Şahin U, Sayili U, et al (2019) Excess Mortality in Istanbul during Extreme Heat Waves between 2013 and 2017. International Journal of Environmental Research and Public Health 16(22):4348. https://doi.org/10.3390/ijerph16224348, URL https://www.mdpi.com/1660-4601/16/22/4348

Carvalho P, Spataru C (2023) Gaps in the governance of floods, droughts, and heat-waves in the United Kingdom. Frontiers in Earth Science 11:1124166. https://doi.org/10.3389/feart.2023.1124166, URL https://www.frontiersin.org/articles/10.3389/feart.2023.1124166/full

Cecinati F, Matthews T, Natarajan S, et al (2019) Mining Social Media to Identify Heat Waves. International Journal of Environmental Research and Public Health 16(5):762. https://doi.org/10.3390/ijerph16050762, URL https://www.mdpi.com/1660-4601/16/5/762

Chambers J (2020) Global and cross-country analysis of exposure of vulnerable populations to heatwaves from 1980 to 2018. Climatic Change 163(1):539-558. https://doi.org/10.1007/s10584-020-02884-2, URL https://link.springer.com/10.1007/s10584-020-02884-2
Cheng Q, Sha S (2024) Resisting the heat wave: Revealing inequalities in matching between heat exposure risk and healthcare services in a megacity. Applied Geography 167:103291. https://doi.org/10.1016/j.apgeog.2024.103291, URL https://linkinghub.elsevier.com/retrieve/pii/S0143622824000961

Corobov R, Sheridan S, Opopol N, et al (2013) Heat-related mortality in Moldova: the summer of 2007: HEAT-RELATED MORTALITY IN MOLDOVA. International Journal of Climatology 33(11):2551–2560. https://doi.org/10.1002/joc.3610, URL https://onlinelibrary.wiley.com/doi/10.1002/joc.3610

Curriero FC (2002) Temperature and Mortality in 11 Cities of the Eastern United States. American Journal of Epidemiology 155(1):80–87. https://doi.org/10.1093/aje/155.1.80, URL https://academic.oup.com/aje/article-lookup/doi/10.1093/aje/155.1.80

Delforge D, Wathelet V, Below R, et al (2025) EM-DAT: the Emergency Events Database. International Journal of Disaster Risk Reduction 124:105509. https://doi.org/10.1016/j.ijdrr.2025.105509, URL https://linkinghub.elsevier.com/retrieve/pii/S2212420925003334

Ellis FP, Princé HP, Lovatt G, et al (1980) Mortality and Morbidity in Birmingham during the 1976 Heatwave. QJM: An International Journal of Medicine https://doi.org/10.1093/oxfordjournals.qjmed.a067600, URL https://academic.oup.com/qjmed/article/49/1/1/1517539/Mortality-and-Morbidity-in-Birmingham-during-the

Fawcett T (2006) An introduction to ROC analysis. Pattern Recognition Letters 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010, URL https://www.sciencedirect.com/science/article/pii/S016786550500303X

Fouillet A, Rey G, Laurent F, et al (2006) Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health 80(1):16–24. https://doi.org/10.1007/s00420-006-0089-4, URL http://link.springer.com/10.1007/s00420-006-0089-4

Founda D, Giannakopoulos C (2009) The exceptionally hot summer of 2007 in Athens, Greece — A typical summer in the future climate? Global and Planetary Change 67(3-4):227–236. https://doi.org/10.1016/j.gloplacha.2009.03.013, URL https://linkinghub.elsevier.com/retrieve/pii/S0921818109000587

Founda D, Santamouris M (2017) Synergies between Urban Heat Island and Heat Waves in Athens (Greece), during an extremely hot summer (2012). Scientific Reports 7(1):10973. https://doi.org/10.1038/s41598-017-11407-6, URL https://www.nature.com/articles/s41598-017-11407-6
Founda D, Katavoutas G, Pierros F, et al (2022) The Extreme Heat Wave of Summer 2021 in Athens (Greece): Cumulative Heat and Exposure to Heat Stress. Sustainability 14(13):7766. https://doi.org/10.3390/su14137766, URL https://www.mdpi.com/2071-1050/14/13/7766

Freychet N, Hegerl GC, Lord NS, et al (2022) Robust increase in population exposure to heat stress with increasing global warming. Environmental Research Letters 17(6):064049. https://doi.org/10.1088/1748-9326/ac71b9, URL https://iopscience.iop.org/article/10.1088/1748-9326/ac71b9

Gasparrini A, Guo Y, Hashizume M, et al (2015) Mortality risk attributable to high and low ambient temperature: a multicountry observational study. The Lancet 386(9991):369–375. https://doi.org/10.1016/S0140-6736(14)62114-0, URL https://linkinghub.elsevier.com/retrieve/pii/S0140673614621140

Giannaros C, Dafis S, Kotroni V, et al (2024) The extreme heat wave of late July/early August 2021 in Greece under the context of the direct effect of anthropogenic greenhouse gases. Atmospheric Science Letters 25(1):e1191. https://doi.org/10.1002/asl.1191, URL https://rmets.onlinelibrary.wiley.com/doi/10.1002/asl.1191

Guigma KH, Todd M, Wang Y (2020) Characteristics and thermodynamics of Sahelian heatwaves analysed using various thermal indices. Climate Dynamics 55(11-12):3151–3175. https://doi.org/10.1007/s00382-020-05438-5, URL https://link.springer.com/10.1007/s00382-020-05438-5

Harrington LJ, Otto FEL (2020) Reconciling theory with the reality of African heatwaves. Nature Climate Change 10(9):796–798. https://doi.org/10.1038/s41558-020-0851-8, URL https://www.nature.com/articles/s41558-020-0851-8

Hersbach H, Bell B, Berrisford P, et al (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146(730):1999–2049. https://doi.org/10.1002/qj.3803, URL https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803

Holtanová E, Valeriánová A, Crhová L, et al (2015) Heat wave of August 2012 in the Czech Republic: comparison of two approaches to assess high temperature event. Studia Geophysica et Geodaetica 59(1):159–172. https://doi.org/10.1007/s11200-014-0805-6, URL http://link.springer.com/10.1007/s11200-014-0805-6

Jones RL, Guha-Sapir D, Tubeuf S (2022) Human and economic impacts of natural disasters: can we trust the global data? Scientific Data 9(1):572. https://doi.org/10.1038/s41597-022-01667-x, URL https://www.nature.com/articles/s41597-022-01667-x

Jones RL, Kharb A, Tubeuf S (2023) The untold story of missing data in disaster research: a systematic review of the empirical literature utilising the Emergency Events Database (EM-DAT). Environmental Research Letters 18(10):103006. https://doi.org/10.1088/1748-9326/acfd42, URL https://iopscience.
Lhotka O, Kyselý J (2022) The 2021 European Heat Wave in the Context of Past Major Heat Waves. Earth and Space Science 9(11):e2022EA002567. https://doi.org/10.1029/2022EA002567, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EA002567

Li N, Thiery W, Zahra S, et al (2025) Wikimpacts 1.0: A new global climate impact database based on automated information extraction from Wikipedia. https://doi.org/10.5194/egusphere-2025-4891, URL https://egusphere.copernicus.org/preprints/2025/egusphere-2025-4891/

Lin YC, Khan F, Jenkins SF, et al (2021) Filling the Disaster Data Gap: Lessons from Cataloging Singapore's Past Disasters. International Journal of Disaster Risk Science 12(2):188-204. https://doi.org/10.1007/s13753-021-00331-z, URL https://link.springer.com/10.1007/s13753-021-00331-z

Lindersson S, Raffetti E, Rusca M, et al (2023) The wider the gap between rich and poor the higher the flood mortality. Nature Sustainability 6(8):995-1005. https://doi.org/10.1038/s41893-023-01107-7, URL https://www.nature.com/articles/s41893-023-01107-7

Liu Zg, Li Xy, Zhu Xh, et al (2022) Towards rainstorm event identification: A transfer learning framework using citizen-report texts and multi-source spatial data. International Journal of Disaster Risk Reduction 83:103427. https://doi.org/10.1016/j.ijdrr.2022.103427, URL https://linkinghub.elsevier.com/retrieve/pii/S221242092200646X

Lloyd-Hughes B (2012) A spatio-temporal structure-based approach to drought characterisation: A STRUCTURE-BASED APPROACH TO DROUGHT CHARACTERISATION. International Journal of Climatology 32(3):406-418. https://doi.org/10.1002/joc.2280, URL https://onlinelibrary.wiley.com/doi/10.1002/joc.2280

Lo YTE, Mitchell DM, Buzan JR, et al (2023) Optimal heat stress metric for modelling heat-related mortality varies from country to country. International Journal of Climatology 43(12):5553-5568. https://doi.org/10.1002/joc.8160, URL https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.8160

Luo M, Lau N, Liu Z, et al (2022a) An Observational Investigation of Spatiotemporally Contiguous Heatwaves in China From a 3D Perspective. Geophysical Research Letters 49(6). https://doi.org/10.1029/2022GL097714, URL https://onlinelibrary.wiley.com/doi/10.1029/2022GL097714

Luo M, Wang X, Dong N, et al (2022b) Two different propagation patterns of spatiotemporally contiguous heatwaves in China. npj Climate and Atmospheric Science 5(1):89. https://doi.org/10.1038/s41612-022-00313-y, URL https://www.nature.com/articles/s41612-022-00313-y
Lüthi S, Fairless C, Fischer EM, et al (2023) Rapid increase in the risk of heat-related mortality. Nature Communications 14(1):4894. https://doi.org/10.1038/s41467-023-40599-x

Ma W, Wang L, Lin H, et al (2015) The temperature–mortality relationship in China: An analysis from 66 Chinese communities. Environmental Research 137:72–77. https://doi.org/10.1016/j.envres.2014.11.016, URL https://linkinghub.elsevier.com/retrieve/pii/S0013935114004319

Madruga De Brito M, Kuhlicke C, Marx A (2020) Near-real-time drought impact assessment: a text mining approach on the 2018/19 drought in Germany. Environmental Research Letters 15(10):1040a9. https://doi.org/10.1088/1748-9326/aba4ca, URL https://iopscience.iop.org/article/10.1088/1748-9326/aba4ca

Madruga De Brito M, Sodoge J, Kreibich H, et al (2025) Comprehensive Assessment of Flood Socioeconomic Impacts Through Text-Mining. Water Resources Research 61(1):e2024WR037813. https://doi.org/10.1029/2024WR037813, URL https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024WR037813

Marx W, Haunschild R, Bornmann L (2021) Heat waves: a hot topic in climate change research. Theoretical and Applied Climatology 146(1-2):781–800. https://doi.org/10.1007/s00704-021-03758-y, URL https://link.springer.com/10.1007/s00704-021-03758-y

Mashhoodi B, Kasraian D (2024) Heatwave exposure inequality: An urban-rural comparison of environmental justice. Applied Geography 164:103216. https://doi.org/10.1016/j.apgeog.2024.103216, URL https://linkinghub.elsevier.com/retrieve/pii/S0143622824000213

Mithal V, Sillmann J, Zscheischler J (2024) Linking regional economic impacts of temperature-related disasters to underlying climatic hazards. Environmental Research Letters 19(12):124010. https://doi.org/10.1088/1748-9326/ad89de, URL https://iopscience.iop.org/article/10.1088/1748-9326/ad89de

O'Reilly C, Larson E, Richards JE, et al (2021) Structural templates for imaging EEG cortical sources in infants. NeuroImage 227:117682. https://doi.org/10.1016/j.neuroimage.2020.117682, URL https://linkinghub.elsevier.com/retrieve/pii/S1053811920311678

Overcenco A, Vera P (2010) Summer heat episodes in Central and Eastern Europe: Czech Republic and Republic of Moldova case. Unpublished https://doi.org/10.13140/2.1.3495.5209, URL http://rgdoi.net/10.13140/2.1.3495.5209, publisher: Unpublished

Paldy A, Bobvos J (2010) Health Impacts of Heat Waves of 2007 in Hungary – Background and Experiences. In: Dincer I, Hepbasli A, Midilli A, et al (eds) Global Warming. Springer US, Boston, MA, p 629–642, https://doi.org/10.1007/
978-1-4419-1017-2_44, URL http://link.springer.com/10.1007/978-1-4419-1017-2_44

Park J, Choi W, Tiesmeyer S, et al (2021) Cell segmentation-free inference of cell types from in situ transcriptomics data. Nature Communications 12(1):3545. https://doi.org/10.1038/s41467-021-23807-4, URL https://www.nature.com/articles/s41467-021-23807-4

Perkins SE (2015) A review on the scientific understanding of heatwaves—Their measurement, driving mechanisms, and changes at the global scale. Atmospheric Research 164-165:242–267. https://doi.org/10.1016/j.atmosres.2015.05.014, URL https://linkinghub.elsevier.com/retrieve/pii/S0169809515001738

Páldy A, Bobvos J (2013) Impact of heat waves on excess mortality in 2011 and 2012 in Hungary. ISEE Conference Abstracts 2013(1):4150. https://doi.org/10.1289/isee.2013.P-3-12-17, URL https://ehp.niehs.nih.gov/doi/10.1289/isee.2013.P-3-12-17

Quilcaille Y, Gudmundsson L, Schumacher DL, et al (2025) Systematic attribution of heatwaves to the emissions of carbon majors. Nature 645(8080):392–398. https://doi.org/10.1038/s41586-025-09450-9, URL https://www.nature.com/articles/s41586-025-09450-9

Reddy PJ, Perkins-Kirkpatrick SE, Sharples JJ (2022) Interactive influence of ENSO and IOD on contiguous heatwaves in Australia. Environmental Research Letters 17(1):014004. https://doi.org/10.1088/1748-9326/ac3e9a, URL https://iopscience.iop.org/article/10.1088/1748-9326/ac3e9a

Rey G, Jougla E, Fouillet A, et al (2007) The impact of major heat waves on all-cause and cause-specific mortality in France from 1971 to 2003. International Archives of Occupational and Environmental Health 80(7):615–626. https://doi.org/10.1007/s00420-007-0173-4, URL https://link.springer.com/10.1007/s00420-007-0173-4

Rooney C, McMichael AJ, Kovats RS, et al (1998) Excess mortality in England and Wales, and in Greater London, during the 1995 heatwave. Journal of Epidemiology and Community Health 52(8):482–486. https://doi.org/10.1136/jech.52.8.482, URL https://jech.bmj.com/lookup/doi/10.1136/jech.52.8.482

Rousi E, Kornhuber K, Beobide-Arsuaga G, et al (2022) Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia. Nature Communications 13(1):3851. https://doi.org/10.1038/s41467-022-31432-y, URL https://www.nature.com/articles/s41467-022-31432-y

Russo S, Dosio A, Graversen RG, et al (2014) Magnitude of extreme heat waves in present climate and their projection in a warming world. Journal of Geophysical Research: Atmospheres 119(22). https://doi.org/10.1002/2014JD022098, URL https://onlinelibrary.wiley.com/doi/10.1002/2014JD022098
Russo S, Sillmann J, Fischer EM (2015) Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters 10(12):124003. https://doi.org/10.1088/1748-9326/10/12/124003, URL https://iopscience.iop.org/article/10.1088/1748-9326/10/12/124003

Sauer IJ, Mester B, Frieler K, et al (2024) Limited progress in global reduction of vulnerability to flood impacts over the past two decades. Communications Earth & Environment 5(1):239. https://doi.org/10.1038/s43247-024-01401-y, URL https://www.nature.com/articles/s43247-024-01401-y

Schiavina M, Freire S, MacManus K (2019) GHS population grid multitemporal (1975-1990-2000-2015), R2019A. https://doi.org/10.2905/0C6B9751-A71F-4062-830B-43C9F432370F, URL http://data.europa.eu/89h/0c6b9751-a71f-4062-830b-43c9f432370f

Schär C, Ban N, Fischer EM, et al (2016) Percentile indices for assessing changes in heavy precipitation events. Climatic Change 137(1-2):201–216. https://doi.org/10.1007/s10584-016-1669-2, URL http://link.springer.com/10.1007/s10584-016-1669-2

Scovronick N, Sera F, Vu B, et al (2024) Temperature-mortality associations by age and cause: a multi-country multi-city study. Environmental Epidemiology 8(5):e336. https://doi.org/10.1097/EE9.0000000000000336, URL https://journals.lww.com/10.1097/EE9.0000000000000336

Semenza JC, Rubin CH, Falter KH, et al (1996) Heat-Related Deaths during the July 1995 Heat Wave in Chicago. New England Journal of Medicine 335(2):84–90. https://doi.org/10.1056/NEJM199607113350203, URL http://www.nejm.org/doi/abs/10.1056/NEJM199607113350203

Seneviratne S, Zhang X, Adnan M, et al (2021) Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st edn. Cambridge University Press, https://doi.org/10.1017/9781009157896, URL https://www.cambridge.org/core/product/identifier/9781009157896/type/book

Sillmann J, Kharin VV, Zhang X, et al (2013) Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of Geophysical Research: Atmospheres 118(4):1716–1733. https://doi.org/10.1002/jgrd.50203, URL https://onlinelibrary.wiley.com/doi/10.1002/jgrd.50203

Silversmith W (2021) cc3d: Connected components on multilabel 3D &amp; 2D images. https://doi.org/10.5281/ZENODO.5719536, URL https://zenodo.org/record/5719536

Simpson NP, Mach KJ, Constable A, et al (2021) A framework for complex climate change risk assessment. One Earth 4(4):489–501. https://doi.org/10.1016/j.oneear.
2021.03.005, URL https://linkinghub.elsevier.com/retrieve/pii/S2590332221001792

Singer M, Hasemann J, Raynor A (2016) "I Feel Suffocated:" Understandings of Climate Change in an Inner City Heat Island. Medical Anthropology 35(6):453-463. https://doi.org/10.1080/01459740.2016.1204543, URL https://www.tandfonline.com/doi/full/10.1080/01459740.2016.1204543

Sippel S, Otto FEL (2014) Beyond climatological extremes - assessing how the odds of hydrometeorological extreme events in South-East Europe change in a warming climate. Climatic Change 125(3-4):381-398. https://doi.org/10.1007/s10584-014-1153-9, URL http://link.springer.com/10.1007/s10584-014-1153-9

Sodoge J, Kuhlicke C, Mahecha MD, et al (2024) Text mining uncovers the unique dynamics of socio-economic impacts of the 2018-2022 multi-year drought in Germany. Natural Hazards and Earth System Sciences 24(5):1757-1777. https://doi.org/10.5194/nhess-24-1757-2024, URL https://nhess.copernicus.org/articles/24/1757/2024/

Stefanon M, D'Andrea F, Drobinski P (2012) Heatwave classification over Europe and the Mediterranean region. Environmental Research Letters 7(1):014023. https://doi.org/10.1088/1748-9326/7/1/014023, URL https://iopscience.iop.org/article/10.1088/1748-9326/7/1/014023

Tolika K, Maheras P, Tegoulias I (2009) Extreme temperatures in Greece during 2007: Could this be a “return to the future”? Geophysical Research Letters 36(10):L10813. https://doi.org/10.1029/2009GL038538, URL http://doi.wiley.com/10.1029/2009GL038538

Tschumi E, Zscheischler J (2020) Countrywide climate features during recorded climate-related disasters. Climatic Change 158(3-4):593-609. https://doi.org/10.1007/s10584-019-02556-w, URL http://link.springer.com/10.1007/s10584-019-02556-w

Twardosz R, Kossowska-Cezak U (2013) Exceptionally Hot Summers Months in Central and Eastern Europe During the Years 1951-2010. In: Dincer I, Colpan CO, Kadioglu F (eds) Causes, Impacts and Solutions to Global Warming. Springer New York, New York, NY, p 17-35, https://doi.org/10.1007/978-1-4614-7588-0_2, URL https://link.springer.com/10.1007/978-1-4614-7588-0_2

Urban A, Hanzlíková H, Kyselý J, et al (2017) Impacts of the 2015 Heat Waves on Mortality in the Czech Republic—A Comparison with Previous Heat Waves. International Journal of Environmental Research and Public Health 14(12):1562. https://doi.org/10.3390/ijerph14121562, URL http://www.mdpi.com/1660-4601/14/12/1562

Vicedo-Cabrera AM, Scovronick N, Sera F, et al (2021) The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate
Change 11(6):492–500. https://doi.org/10.1038/s41558-021-01058-x, URL https://www.nature.com/articles/s41558-021-01058-x

Vogel MM, Zscheischler J, Fischer EM, et al (2020) Development of Future Heatwaves for Different Hazard Thresholds. Journal of Geophysical Research: Atmospheres 125(9). https://doi.org/10.1029/2019JD032070, URL https://onlinelibrary.wiley.com/doi/abs/10.1029/2019JD032070

Výberči D, Švec M, Faško P, et al (2015) The effects of the 1996–2012 summer heat events on human mortality in Slovakia. Moravian Geographical Reports 23(3):58–70. https://doi.org/10.1515/mgr-2015-0018, URL https://www.sciendo.com/article/10.1515/mgr-2015-0018

van der Wiel K, Selten FM, Bintanja R, et al (2020) Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions. Environmental Research Letters 15(3):034050. https://doi.org/10.1088/1748-9326/ab7668, URL https://iopscience.iop.org/article/10.1088/1748-9326/ab7668

Yang J, Yin P, Sun J, et al (2019) Heatwave and mortality in 31 major Chinese cities: Definition, vulnerability and implications. Science of The Total Environment 649:695–702. https://doi.org/10.1016/j.scitotenv.2018.08.332, URL https://linkinghub.elsevier.com/retrieve/pii/S0048969718333035

Zhang B, Schilder F, Smith KH, et al (2022) TweetDrought: A Deep-Learning Drought Impacts Recognizer based on Twitter Data. https://doi.org/10.48550/ARXIV.2212.04001, URL https://arxiv.org/abs/2212.04001, version Number: 1

Zhang X, Alexander L, Hegerl GC, et al (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Climate Change 2(6):851–870. https://doi.org/10.1002/wcc.147, URL https://onlinelibrary.wiley.com/doi/10.1002/wcc.147

Zhao Q, Guo Y, Ye T, et al (2021) Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. The Lancet Planetary Health 5(7):e415–e425. https://doi.org/10.1016/S2542-5196(21)00081-4, URL https://linkinghub.elsevier.com/retrieve/pii/S2542519621000814

Zscheischler J, Mahecha MD, Harmeling S, et al (2013) Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecological Informationics 15:66–73. https://doi.org/10.1016/j.ecoinf.2013.03.004, URL https://linkinghub.elsevier.com/retrieve/pii/S1574954113000253

Zscheischler J, Mahecha MD, von Buttlar J, et al (2014a) A few extreme events dominate global interannual variability in gross primary production. Environmental Research Letters 9(3):035001. https://doi.org/10.1088/1748-9326/9/3/035001, URL https://iopscience.iop.org/article/10.1088/1748-9326/9/3/035001
Zscheischler J, Reichstein M, Harmeling S, et al (2014b) Extreme events in gross pri-
mary production: a characterization across continents. Biogeosciences 11(11):2909-
2924. https://doi.org/10.5194/bg-11-2909-2014, URL https://bg.copernicus.org/
articles/11/2909/2014/

Zschenderlein P, Fink AH, Pfahl S, et al (2019) Processes determining heat waves across different European climates. Quarterly Journal of the Royal Meteorological Society 145(724):2973–2989. https://doi.org/10.1002/qj.3599, URL https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3599

# 6 Statements and Declarations

**Funding:** This work was supported by the European Union (Grant Agreement No. 101003469, XAIDA), and the National Research Agency (Grant ANR-22-EXTR-0005, TRACCS-PC4-EXTENDING project).

**Competing Interests:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
<table><caption>**Table 5** Top 10 CC3D heatwaves unmatched in EM-DAT: investigation in the literature and press. Country names are replaced by their ISO 3166-1 alpha-3 codes. The rank corresponds to the rank among the 199 CC3D heatwaves, so the missing numbers correspond to heatwaves that overlap with EM-DAT. The column "Deaths" informs on the presence of quantitative death records in the corresponding references. More details are given in SM2.</caption><thead><tr><th>Dates</th><th>Rank</th><th>Affected countries</th><th>Deaths</th><th>Scientific references</th><th>Other press ref</th></tr></thead><tbody><tr><td>1976/06/22 to 1976/07/09</td><td>7</td><td>BEL, CZE, DNK, FRA, DEU, IRL, LUX, NLD, CHE, GBR</td><td>Yes</td><td>Ellis et al. (1980), Rey et al. (2007)</td><td>BEL, FRA</td></tr><tr><td>1994/07/20 to 1994/08/04</td><td>11</td><td>AUT, BLR, CZE, DNK, EST, DEU, HUN, LVA, LTU, POL, ROU, RUS, SRB, SVK, SWE, UKR</td><td>Yes</td><td>Overcenco and Vera (2010), Urban et al. (2017)</td><td></td></tr><tr><td>1995/07/27 to 1995/08/26</td><td>17</td><td>IRL, GBR</td><td>Yes</td><td>Rooney et al. (1998)</td><td></td></tr><tr><td>2007/08/08 to 2007/08/28</td><td>10</td><td>ALB, BLR, BIH, BGR, EST, FIN, GRC, LVA, LTU, MKD, MDA, MNE, ROU, RUS, SRB, TUR, UKR</td><td>Yes</td><td>Tolika et al. (2009), Founda and Gian-nakopoulos (2009), Paldy and Bobvos (2010), Twardosz and Kossowska-Cezak (2013), Corobov et al. (2013)</td><td></td></tr><tr><td>2012/06/29 to 2012/07/16</td><td>15</td><td>ALB, AUT, BLR, BIH, BGR, HRV, CZE, GRC, HUN, MKD, MDA, MNE, POL, ROU, RUS, SRB, SVK, SVN, TUR, UKR</td><td>Yes</td><td>Páldy and Bobvos (2013), Výberči et al. (2015)</td><td></td></tr><tr><td>2012/08/17 to 2012/08/27</td><td>12</td><td>ALB, AUT, BIH, BGR, HRV, FRA, DEU, GRC, HUN, ITA, MKD, MDA, MNE, ROU, SRB, SVN, ESP, CHE, TUR, UKR</td><td>Yes</td><td>Páldy and Bobvos (2013), Sippel and Otto (2014), Holtanová et al. (2015), Founda and Santamouris (2017), Lhotka and Kyselý (2022), Výberči et al. (2015)</td><td>FRA</td></tr><tr><td>2015/08/04 to 2015/08/16</td><td>9</td><td>AUT, BLR, HRV, CZE, DEU, HUN, LVA, LTU, MDA, POL, ROU, RUS, SRB, SVK, SVN, UKR</td><td>Yes</td><td>Urban et al. (2017)</td><td></td></tr><tr><td>2017/07/30 to 2017/08/14</td><td>6</td><td>ALB, AUT, BIH, BGR, HRV, FRA, GRC, HUN, ITA, MKD, MDA, MNE, POL, ROU, RUS, SRB, SVK, SVN, CHE, TUR, UKR</td><td>No</td><td>Can et al. (2019)</td><td>South Europe</td></tr><tr><td>2021/06/14 to 2021/07/19</td><td>4</td><td>ALB, AUT, BLR, BIH, BGR, HRV, CZE, EST, FIN, FRA, DEU, GRC, HUN, LVA, LTU, LUX, MKD, MNE, NOR, POL, ROU, RUS, SRB, SVK, SVN, SWE, CHE, UKR</td><td>No</td><td>Lhotka and Kyselý (2022)</td><td>RUS, Europe</td></tr><tr><td>2021/07/25 to 2021/08/16</td><td>13</td><td>ALB, BIH, BGR, HRV, FRA, GRC, ITA, MKD, MNE, ROU, SRB, CHE, TUR</td><td>Yes</td><td>Founda et al. (2022), Giannaros et al. (2024)</td><td>ITA, GRC, TUR</td></tr></tbody></table>