The heatwave has affected multiple regions within Europe, likely inducing impacts ranging from increased mortality and health issues⁴, to forest fires in France⁵, electricity black outs in Italy⁶, and infrastructure breakdowns in France⁷. Due to the drought in the Mediterranean, the heatwave has even higher impacts. Italy was facing energy security issues, as a result of decreasing hydropower electricity production due the drought, which is especially worrisome with Italy trying to wean off Russia’s gas⁸. On top of that, the extreme heat caused an increase in energy demand, pushing electricity grids beyond their limits leading to blackouts in multiple cities⁶. Besides electricity, the agricultural sector is also hit hard by the compounding drought and heatwave conditions, potentially decreasing harvest by 30-40% in the Po Valley, Italy⁹. The Po Valley is an important agricultural region for both Italy and Europe, accounting for 35% of Italian agriculture¹⁰ and 50% of Europe’s rice¹¹. 

Links to climate change

The 6th IPCC assessment report stated that human induced climate change is the main driver of the increase in frequency and intensity of heat extremes (virtually certain)¹². This  increase in frequency and intensity of heat extremes will continue with rising temperatures¹². For example, in the Mediterranean a one in 50 year heatwave event will become more than 15 times more likely under 1.5 degree warming, and more than 30 times more likely under 2.0 degree warming¹². These statements raise the question whether the specific June 2022 heatwave, with its early character, has become more likely due to global warming. 

Here, we compare the occurrences of extreme heat events from 5 different regions and 2 additional cities, as shown in Figure 1, in pre-industrial and present-day climate. This is called Extreme Event Attribution¹³, a method that allows for the attribution of a specific class of extreme events to climate change, examining changes in both its frequency and intensity. 

Several approaches allow us to assess the role of climate change in the development of a specific event or class of event. First,  the event is defined using one or several indices. Then, the “statistical” approach models the extreme values of the indices, as obtained from observations or climate simulations, using asymptotic extreme value distributions. Last, the  calculated return periods in a factual and counterfactual climate are compared. The “storyline” approach, in contrast, simulates how the current event was modified by greenhouse gasses using sensitivity experiments on boundary conditions (Sea Surface Temperatures, greenhouse gases, etc…) in order to disentangle the role of climate change.

Here we used the statistical approach and focused on heat in the month of June in the previously mentioned areas in Europe. Yearly maxima of 1-day, 3-day, or 5-day June maximal temperatures are fitted using the Generalized Extreme Value (GEV) distribution, with a proxy of climate change as a covariate (here the smoothed global mean surface temperature), using the methodology developed in ¹³, which is the basis of rapid statistical event attribution (https://www.worldweatherattribution.org). This allows us to simulate distribution of extremes in the present-day climate and in a climate globally 1.2°C cooler (pre-industrial conditions) (see for example Figure 4). This is done for observational and model data, after which the return period, change in intensity, and change in probability, as well as their uncertainties, are calculated for the extreme event using a weighted average, accounting for both inter-model variability and sampling uncertainties.