In the present study, we investigate the exceptional nature of the 2018 north hemispheric heat wave in the context of anthropogenic climate change. We first synthesize reported heat‐related impacts across the entire Northern Hemisphere in the spring to summer season. Subsequently, we investigate the fraction of the land area in the northern midlatitudes that is either densely populated or used for agricultural production and that was affected by extremely hot conditions. We then study whether current Earth System Models simulate a 2018‐like area fraction under present‐day conditions and whether the event could have occurred without climate change. Finally, we provide estimates of how such an event will develop under unabated global warming.

In the future, the intensity, frequency, and global heat wave area over land are projected to drastically increase (Coumou & Robinson, 2013 ; Christidis et al., 2014 ). These projected increases can strongly affect future heat wave impacts and risk of concurrent extremes. While locally confined climate extremes often lead to devastating impacts at the local scale, spatially correlated extremes can have much more severe societal impacts due to their effect on integrated variables. For instance, concurrent heat waves in different agricultural regions can affect global food production (Sarhadi et al., 2018 ) and consequently increase global food prizes and food insecurity (Tigchelaar et al., 2018 ). Similarly, synchronized river floods can cause excessive damages (Berghuijs et al., 2019 ).

Given the tremendous impacts of these heat waves, the question arises how much they can be attributed to human activities. Most recent heat waves have been attributed to a smaller or larger extent to anthropogenic climate change (NAS, 2016 ). For instance, anthropogenic climate change has tripled the occurrence rate of heat waves similar to the 2010 heat wave in Russia (Otto et al., 2012 ; Rahmstorf & Coumou, 2011 ). Heat waves of the magnitude of the Indian heat wave in May/June 2015 are 2 to 8 times more likely due to human‐induced climate change (Wehner et al., 2016 ). A near‐real‐time attribution study concluded that the extreme heat in northern Europe in the 2018 summer was on average 2 times more likely due to human‐induced climate change (World Weather Attribution, 2018 ). However, in these cases, as in many other heat wave attribution studies, natural variability cannot be excluded as a driver.

Record‐breaking temperatures occurred concurrently in multiple regions including North America, Europe, and Asia in late spring/summer 2018 (NOAA, 2018a , 2018b , 2018c ). Europe experienced late spring and summer temperatures that were more than 1 °C warmer than 1981–2010 (Copernicus, 2019 ). The contiguous United States had the warmest May since 1895 (NOAA, 2018c ), and the hottest month ever observed was in July in the Death Valley (NOAA, 2018a ). The 2018 hot temperatures are in line with an increase in intensity and frequency of extreme heat events over many regions on land and in the ocean in recent years (Coumou & Rahmstorf, 2012 ; Christidis et al., 2014 ; Fischer & Knutti, 2015 ; Frölicher et al., 2018 ; Rowe & Derry, 2012 ; Seneviratne et al., 2014 ). Owing to their devastating impacts, understanding changes in extreme temperature events is highly relevant for society and ecosystems. Recent heat waves with particularly severe impacts include the 2010 Russian and 2015 Indian heat waves. The 2010 Russian heat wave was associated with the death of tens of thousands of people, major crop failure, millions of hectares affected by fires, and around 15 billion U.S. dollar economic loss (Barriopedro et al., 2011 ). During the 2015 heat wave in India, at least 2,500 people died (Ratnam et al., 2016 ). Impacts were particularly severe because they occurred in agricultural regions and/or regions with high population density.

We estimate global mean temperature increase in CMIP5 models by computing 31‐year moving averages of global mean temperature ( T glob ) for all models between 1958 and 2100 and determine changes relative to the observational reference period 1958–1988. We estimate T glob as weighted mean between tas over land and (dynamic) sea ice regions and sea surface temperature ( tos in CMIP5) over the oceans, similar to observations (also called “blended temperatures” Cowtan et al., 2015 ). The multimodel mean warming between the reference period and 1870–1900 is +0.28 °C and is added to changes relative to the reference period. The warming levels of 1, 1.5, and 2 °C are determined as 31‐year periods such that T glob in the center year is closest to the respective warming level. For the scaling of T glob with the concurrent hot day area, we relate the center year of each 31‐year mean T glob with the its respective concurrent hot day area. We use a distance weighting of each individual model to obtain values closest to T glob and then compute multimodel median and interquartile range. We then apply a linear filter by 0.1 °C to smooth the data and improve the visual appearance.

To estimate the human influence on climate extremes, a common approach is to compute the change in likelihood of events such as heat waves, droughts, and heavy precipitation events. Hereby, the ratio is calculated between the likelihood of the event in a climate with anthropogenic forcing ( p CC ) and the likelihood in a reference period with smaller or no anthropogenic forcing ( p 0 ; Fischer & Knutti, 2015 ; NAS, 2016 ; Stott et al., 2004 , 2016 ). We quantify to what extent anthropogenic climate change has contributed to the probability of the GCWH18 event. To this end, we calculate the probability p 0 that the concurrent hot day area of 2018 would have occurred with little/without anthropogenic climate change. We further compute the probability p cc that the concurrent hot day area of 2018 occurred with climate change. The ratio between the two probabilities , often referred to as risk ratio or probability ratio, allows statements about the human contribution to the event (NAS, 2016 ). If p 0 =0, that is, the probability ratio equals ∞ , the event could not have occurred without anthropogenic climate change. We compute p 0 for exceeding different concurrent hot day areas between 0% and 65% for the reference period (1958–1988) for CMIP5 models and observations. Since the climate has already warmed since preindustrial times by +0.28 °C, we perform the same computation for a preindustrial period (1870–1900). We further compute p cc for different concurrent hot day areas for 31‐year periods in which global warming reached +1 °C, +1.5 °C, and +2 °C for the same area thresholds (this results in slightly different 30‐year periods for each model). For comparison, we also compute p cc for the time period 1988–2018 ( supporting information Figure S3 ).

The daily concurrent hot day area fraction over the target region is illustrated in Figure 1 a. For the base period from 1958 to 1988 the daily area fraction that is concurrently affected by extreme warm or hot conditions covers on average 10% of the focus region (since we define hot day based on 90th percentile exceedances; see section 2.5 ). The 2018 area fraction exceeds the 1958 to 1988 areas by around a factor of 2 on average every day between May and July. The daily 2018 area peaks in the end of July reaching a maximum of 37.5% (Figure 1 b). Hereafter, we focus on the temporal average between May and July 2018 as considered Global Concurrent Warm and Hot 2018 event, in short, GCWH18 extreme event.

In this study we investigate the extensive spatially distributed heat impacts of the 2018 boreal summer as a single event. We compute the daily 90th temperature percentile using a 31‐day moving window for the reference period 1958–1988, resulting in a 90th percentile climatology of the reference period for each model and the observed data. Each day between May and July that exceeds the temperature of the respective calendar day in the 90th percentile climatology is defined as a hot day. We then regrid results from each model to the NH AgPop area grid and only consider the NH AgPop area for subsequent analyses. The average concurrent hot day area of a year is then defined as the average daily spatial extent of locations that experience a hot day between May and July. To obtain relative concurrent hot day areas, the average yearly concurrent hot day area is divided by the total area of the NH AgPop area. In addition to the concurrent hot day area between May and July, we also compute the concurrent hot day area between May to August and May to September ( supporting information Figure S2 ).

Considered hot day area and time frame. (a) Regions north of 30° latitude that either have a high population density (at least 30 people per square kilometer) or are agricultural regions (NH AgPop area) are highlighted in gray. (b) Annual time series of daily concurrent hot day area fraction of the NH AgPop area for 1959–1988 (gray) and 2018 (purple).

We focus on latitudes north of 30° of the “human‐affected and human‐affecting” regions that either have a high population density (above 30 km −2 ) or are important agricultural areas as defined in Seneviratne et al. ( 2018 ) with a total area of 23.7×10 6 km 2 . This study region is referred to as NH AgPop area hereafter (Figure 1 a).

We use single runs from 29 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) from the historical and future simulations (Taylor et al., 2012 ) under a high‐emission scenario (Representative concentration pathway 8.5 [RCP8.5]) from 1870 to 2100 (van Vuuren et al., 2011 ). We analyze daily air temperature ( tas in CMIP5) over land to estimate concurrent hot day areas and also used sea surface temperature ( tos in CMIP5) for computing global mean temperature (see below). The complete list of models is provided in supporting information Table S1 .

As an observational data basis, we use daily temperature from ERA‐40 (40‐year European Centre for Medium‐Range Weather Forecasts Reanalysis; Uppala et al., 2005 ) for the period 1958–1999 and ERA‐Interim (European Centre for Medium‐Range Weather Forecasts Interim reanalysis; Dee et al., 2011 ) data for the period 1979–2018. We merged the ERA‐40 and ERA‐Interim data to cover a period from January 1958 to December 2018, with ERA‐Interim starting in January 1979. We tested the consistency between ERA‐40 and ERA‐Interim by computing the root‐mean‐square error and mean bias between both data sets for the overlapping period (1979–1999). Both metrics show rather small difference between the data sets, in particular in the study region (see section 2.4 and supporting information Figure S1 ).

We collected information from newspaper articles about heat‐related impacts from the 2018 event. This does not include information about record‐breaking temperatures, which were much more widespread. However, an exhaustive media overview was not the scope of this study.

In a +1.5 and +2 °C world, the probability of experiencing a heat wave with at least the size of the GCWH18 event increases to 65% (Figure 6 b) and 97% (Figure 6 c) in the multimodel median, respectively. This implies that even if global warming is limited to +1.5 °C, we would experience a GCWH18‐type event on average nearly 2 out of 3 years and nearly every year at +2 °C warming.

North hemispheric concurrent hot day area thresholds (May to July) relative to the full NH AgPop area (0% to 65%) versus multimodel range of probabilities of exceeding that concurrent hot day area for global warming of +1 °C (a), +1.5 °C (b), and +2 °C (c) with respect to 1870–1900. The GCWH18 area is highlighted by a purple vertical dashed line in each subpanel. The boxplots indicate the distributions of the exceedance probabilities of the multimodel ensemble for the 2018‐like concurrent hot day area (22%) for the different warming levels.

North hemispheric 2018‐like or larger concurrent hot day areas are rare in today's climate (+1 °C mean warming; IPCC, 2018 ), with a likelihood of occurrence of 16% in the multimodel median (Figure 6 a). Using a present‐day period from 1988 to 2018 (with RCP8.5 forcing after 2005), the multimodel median probability of a GCWH18‐like event is reduced to 6% ( supporting information Figure S3 ).

The 2018 north hemispheric concurrent heat extremes in an attribution framework. Shown are average north hemispheric concurrent hot day area thresholds (May to July) relative to the full NH AgPop area (0% to 65%) versus probabilities of exceeding that concurrent hot day area. The GCWH18 area is highlighted by a purple vertical dashed line in each subpanel. (a) Probabilities for exceeding concurrent hot day areas in the reference period 1958–1988 () for the multimodel ensemble (gray range) and observations (black line). The gray arrow indicates concurrent hot day area thresholds where=0, and thus, the probability ratiois infinity independent of. (b) Probabilities for exceeding concurrent hot day areas in the preindustrial period 1870–1900 (). Each gray line represents one model simulation (=29). (c) Multimodel range of probabilities for exceeding concurrent hot day areas for global warming of +1 °C (orange), +1.5 °C (red), and +2 °C (dark red) with respect to 1870–1900. The boxplots indicate the distributions of the exceedance probabilities of the multimodel ensemble for the 2018‐like concurrent hot day area (22%) for the different warming levels.

The intense warming over the last decades has strongly shifted the odds of experiencing a GCWH18‐like event, and we determine the human influence on a GCWH18‐like area. A 2018‐like event with an average 22% concurrent hot day area between May and July is not uncommon in climate simulations of a present‐day climate with anthropogenic forcing ( p CC >0). However, it does not occur in the model simulations of the reference period about 50 years earlier with much smaller anthropogenic forcing( p 0 =0). Thus, conditional on the analyzed climate models, the probability ratio of this event is infinity. Hence, the analysis based on the 1958–1988 reference period suggests that average concurrent hot day areas that exceed 20% of the NH AgPop area could not have occurred without human‐induced climate change. For the reference period, the observed probabilities of exceeding specific average concurrent hot day areas fall within the model range, which provides confidence in the model simulations of concurrent hot day areas with large extent (Figure 5 a). However, this reference period is relatively short and already includes a mean warming signal of about 0.3 °C compared to the preindustrial period 1870 to 1900. During that period, the occurrence of a 2018‐like heat event occurs in 2 years in one out of 29 models (Figure 5 b). Hence, considering all model simulations, this results in an approximately 0.2% probability for a GCWH18‐like area to have occurred under preindustrial conditions. These findings confirm that it is virtually certain (Mastrandrea et al., 2010 ) that the 2018 heat event would not have occurred without human‐induced greenhouse gas emissions.

For the high‐emissions scenario (RCP8.5, section 2.3 ), the average concurrent hot day area during May and July is projected to cover more than 70% (multimodel median) of the NH AgPop area every day at the end of the 21st century (Figure 4 b). The average concurrent hot day area scales approximately linearly with global mean temperature increases between +1 and +4 °C, with an increase of about 16% of the NH AgPop area per degree Celsius warming (Figure 4 c). The warming in 2018 in the CMIP5 models since 1870–1900 is about +1.2 °C, which is slightly larger than current estimates based on observations (Intergovernmental Panel on Climate Change [IPCC] , 2018 ) and is related to data coverage, the choice of reference period, and interannual variability (see section 4 ).

Temporal evolution of average concurrent hot day areas between May and July in observations and Earth system models. (a) Observed average concurrent hot day area between May and July in percent of the NH AgPop area for the time period 1958 to 2018. (b) Modeled average concurrent hot day area from 1958 to 2100 based on Coupled Model Intercomparison Project phase 5 models. The observed time series is shown in black; the range of (a) is highlighted in gray. (c) Modeled concurrent hot day area as a function of global mean temperature () increase (see section 2.8 ). The 2018 event is highlighted by a horizontal purple line in each subpanel. For the models, we used a high‐emission scenario (RCP8.5). We show the model median (gray), interquartile range (red), and the full model range (yellow).

Between May and July 2018, a daily average of about 22% of the NH AgPop area experienced a heat wave (Figure 4 a). This extreme spatial extent of the GCWH18 event is unprecedented in the observational record when considering heat wave between May and July and nearly 8% larger then the second largest event of this kind in 2012. Computing the concurrent hot day area between May and August, 2010 and 2018 have a similar extent (23% and 22%; Figure S2 a) and also for May to September (both 21%; Figure S2 b).

Between May and July 2018, some regions experienced hot days for up to 2 months, including Scandinavia, central Europe, and the United States (Figure 3 a). The mean temperature anomaly of the hot days in these regions was up to 1.4 °C warmer than the 90th percentile daily temperatures in the reference period (Figure 3 b). Owing to the associated exposure, heat wave impacts were particularly severe over the NH AgPop regions (see section 2.5 ).

Heat‐related impacts in 2018 as reported by news agencies. (a) Approximate locations of heat‐related impacts in the northern midlatitudes (above 30° north). The impacts are categorized according to heat impact (cross, purple text), fires (fire, red text), agricultural and ecological damages (wheat, orange text), damages to infrastructure (railway track, brown text), and impacts on power production reduction/shortage (warning signal, blue text). (b) Detailed heat‐related impacts per country. The color refers to the categories in (a). The sources where this information is taken from are listed in Table 1

Between May and July 2018, heat‐related impacts have been described for at least 18 countries in the northern midlatitudes for different sectors, Figure 2 showcases a number of reported impacts. The URLs and news agencies of the media articles are provided in Table 1 . Affected countries include Canada and the United States in North America, many European countries, as well as Russia, China, Japan, and South Korea in Asia. The wide spatial extent of impacts illustrates the global dimension of the extreme temperatures. News agencies reported direct heat‐related impacts on humans and also on agriculture, power plants, and infrastructure (Figure 2 b). For instance, in Quebec, more than 70 people died from heat strokes. In the United States, around 35 million people lived with regular heat warnings through July. In Japan, at least 30 people died and more than 2,500 were sent to hospital. Devastating fires destroyed vast amounts of intact forests in Canada, the United States, Norway, Sweden, Finland, Latvia, Greece, and South Korea. The hot temperatures also caused crop loss particularly for grains in Germany, Russia, Switzerland, and the United Kingdom. The heat wave conditions further led to power shortages and reduction in power production in the United States, Germany, France, and Switzerland. Roads melted and train tracks buckled in the Netherlands and the United Kingdom.

4 Discussion and Conclusions

Over the recent decades, we have seen a strong increase in land areas that are concurrently affected by warm and hot temperatures (Figure 4a). The four largest events in the observational record occurred within the last 12 years, consistent with a strong recent increase in heat wave occurrence (Christidis et al., 2014; Rowe & Derry, 2012; Seneviratne et al., 2014). Generally, the large interannual variability of concurrent heat wave occurrence can be driven by different processes. While years with El Niño events are typically associated with warmer temperatures at a global scale (Seneviratne et al., 2014), this effect is less relevant for the boreal summers in the NH AgPop region. Furthermore, changes in large‐scale circulation and land‐atmosphere feedbacks could increase the areas concurrently experience warm and hot temperatures. A recent study shows that in 2018 a specific atmospheric pattern connected heat waves in North America, Western Europe, and the Caspian Sea region (Kornhuber et al., 2019). Additionally, central Europe experienced concurrent dry and hot conditions from spring to summer in 2018 that were exceptional at least within the last 500 years (Toreti et al., 2019). The dry conditions in central and Northern Europe possibly amplified extreme temperatures in these regions via land‐atmosphere feedbacks.

Overall, our results suggest that we have entered a new climate regime in which the occurrence of extraordinary global‐scale heat waves cannot be explained without human‐induced climate change. In particular, the simulations show that a GCWH18‐like event does not occur in historical simulations. The saturation effect of the probability ratio will become more dominant in a warmer climate where unprecedented events will have a probability ratio in the mathematical limit of (Harrington & Otto, 2018).

The importance of global warming targets for limiting the occurrence of global‐scale heat waves becomes even more apparent when considering climate model projections. We find that the probability of devastating 2018‐like areas to occur will increase drastically (from 65% to 97%) if global mean temperatures shift from +1.5 °C to +2 °C. This is consistent with the recently approved IPCC special report on Global Warming of 1.5 °C, which finds significantly stronger climate‐related impacts for +2 °C compared to +1.5 °C warming (IPCC, 2018).

However, determining impacts conditioned on global mean temperature targets is not free of uncertainties. Using only 2‐m air temperature (tas in CMIP5) for computing T glob leads to an overestimation of the CMIP5 multimodel median of global mean temperature of 0.18 °C compared to observed temperatures for the time period 2000–2009 with respect to 1861–1880 (Richardson et al., 2016). This offset is partly related to a reduced coverage in the observations (Cowtan et al., 2015). For instance, Antarctica is not well covered by observations and warms faster than the global average (Cowtan & Way, 2014). It is not clear how the coverage will change in future; hence, here we consider the whole globe instead of assuming future changes in coverage. In addition to the effects of data coverage, observation‐based estimates of global mean temperature use air temperature over land and sea ice but sea surface temperatures over oceans (Cowtan et al., 2015; Medhaug et al., 2017; Richardson et al., 2016). To account for this, we compute T glob as “blended temperatures” (Cowtan et al., 2015), that is, using air temperature over land and (dynamic) sea ice regions and sea surface temperature over the oceans (see section 2.8). This leads to a present‐day warming of +1.22 °C compared to +1.29 °C when only tas is considered (supporting information Figure S3). Further differences between models and observations can be related to internal climate variability, overestimated radiative forcing in models, and model response error (Flato et al., 2013). Observations themselves are also uncertain with differences in mean global warming in the early 2000s of up to +0.4 °C (Medhaug et al., 2017). Finally, the global mean temperature change also depends on the preindustrial reference period, which can lead to a shift of a decade to reach a specific warming level (Hawkins & Sutton, 2016). Overall, because of these systematic differences between CMIP5 models and observations, our estimates of future exceedance probabilities at specific global mean temperature targets are conservative estimates.

Our results demonstrate that the GCWH18 extreme event could not have occurred without human‐induced climate change. Due to taking averages over large areas, the signal‐to‐noise ratio is high for such events, and we could exclude that it could have occurred as part of natural variability. To our knowledge, so far only for marine heat waves, it could be concluded that their occurrence would not have been possible without climate change (Oliver et al., 2018; Perkins‐Kirkpatrick et al., 2019). Thus, the GCWH18 event possibly constitutes the first climate phenomenon on land that has been uniquely attributed to human‐induced global warming.

Heat waves will likely reach highly dangerous levels for ecosystems and societies over the coming decades. However, if other factors that contribute to such impacts change as well, assessing environmental risks associated with heat waves becomes much more complex. For instance, the impact of expanding concurrent hot day areas is often aggravated by population increases. In the United States, population exposure to extreme heat has increased fourfold to sixfold in the late twentieth century due to a combined increase in temperatures and population growth (Jones et al., 2015).

In contrast to the local heat impacts on population, widely spread agricultural regions can serve as an integrator of heat‐related impacts on crop yields (Lobell & Field, 2007). Concurrent heat extremes in different agricultural regions can amplify heat impacts as simultaneous production shocks can increase global food prizes and food insecurity (Tigchelaar et al., 2018). Multiple heat waves that occur subsequently in the same region can amplify heat wave impacts and are projected to increase with global warming (Baldwin et al., 2019). Furthermore, a higher probability of experiencing a heat wave naturally increases the likelihood of compound extremes. In particular, the co‐occurrence rate of compound drought‐heat wave events will increase even if there is no trend in drought occurrence (Diffenbaugh et al., 2015; Zscheischler & Seneviratne, 2017) and may exacerbate heat‐related impacts (Miralles et al., 2018; Seneviratne et al., 2010). Such compound events in space and time are challenging to analyze but require immediate attention because climate change will shift many aspects of these events (Zscheischler et al., 2018).

The provided scaling relationship for concurrent hot day areas (Figure 4c) can help to assess future risk of heat‐related impacts. However, it also illustrates that concomitant heat waves in large parts of the inhabited and agricultural land area in the northern midlatitudes can be effectively prevented by limiting global warming. To avoid major future impacts associated with human‐induced global heat waves, we have to take ambitious mitigation actions to strongly reduce greenhouse gas emissions.