But let’s start from the evidence. Surface temperatures in the Mediterranean have been above average, almost constantly, for years now. This plot shows the average sea surface temperature (obtained from satellite retrievals) as a seasonal and areal mean over the Mediterranean.

As we can see, the last season with below-average sea surface temperatures was at the beginning of 2022. What strikes even more is how widely the anomalous temperatures have spread, something a simple basin-average cannot show. So in this additional plot I computed the percentage of the Mediterranean basin where the temperature is above its local 90th percentile. This is not necessarily unusual in a single spot, but it should not happen too often over a wide area.

Before the infamous summer of 2003, this value mostly oscillated below 20%. After 2012 it started regularly exceeding 20%, especially during summer. After 2022, we entered what looks like a completely different regime: winters now regularly sit above 50% of the basin. In other words, the Mediterranean has no time to recover from the summer, and it keeps accumulating heat.

Why show sea temperatures instead of atmospheric ones? Because the Mediterranean, like other seas, acts as a huge reservoir of heat and moisture, which can then be turned into precipitation and instability, i.e. thunderstorms. But note the “can”: this is a potential, and it may not turn into anything we can observe.

One thing is clear: the warmer basin comes with a lot more water vapour. The following plot shows the anomaly of the atmospheric water vapour content, derived from reanalysis. It is usually called Precipitable Water (PWAT), and it can be read directly in mm: it is the rain that would fall if all the vapour in the atmospheric column condensed at once.

Of course, this isn’t just the effect of the warmer sea (which causes more evaporation). A warmer atmosphere can also hold more water vapour.

A side effect of a warmer sea and a moister atmosphere is more potential energy available for thunderstorms. To estimate it, meteorologists look at the entire atmospheric column and integrate the energy that a parcel rising from the surface would gain under ideal conditions. The result is called Convective Available Potential Energy (CAPE), in J/kg. Note the word “Available”: the energy can be used if a thunderstorm forms, but nothing happens without a proper trigger.

The next plot shows the share of the Mediterranean where CAPE exceeds 3500 J/kg. It’s an extremely high value: if a storm does fire in such an environment, it is very likely to be a severe one.

The seasonal average of the affected area reached a new high exactly this summer, and the ongoing autumn (data until 19 October, so still incomplete) is on track to set a record too. It’s hard to see in this plot, but there is a significant upward trend in summer (+0.7 percentage points of area per decade, p < 0.01) and in autumn (+0.19 pp/decade, p = 0.01).

Now comes the harder part. How do we measure the effect of this accumulation of heat, moisture and instability?

Let’s start with precipitation. Several sources indicate a clear upward trend, in agreement with Clausius-Clapeyron scaling. Station data are sparse and inhomogeneous, though very accurate for local extremes. Radar is homogeneous and accurate, but its archives are too short to derive trends. So I used a satellite precipitation product (IMERG), which has been around long enough (since 2001).

To focus on extremes, I looked at the area where the daily maximum rain rate exceeds its local 99th percentile, computed on wet days only. It’s the same idea as for sea temperatures, but much more selective, because we want the rare, intense events. One caveat: the baseline here is 2001-2020, while SST, water vapour and CAPE use 1991-2020, since IMERG doesn’t go back further.

In the seasonal plot, winter 2026 and spring 2025 stand out as the extremes. It’s hard to tell from this plot alone, but there is a clear upward trend in all seasons, stronger in winter and spring. So far, the effect shows up in rain.

But what about thunderstorms?

This is where things get complicated. It is quite hard to “measure” thunderstorm activity over time. Lightning data could do it, but ground networks suffer from changing detection efficiency and receiver density, while satellite lightning detectors only have a few years of data.

Instead, we can detect thunderstorms directly from satellite, using channels that were on board even older instruments. Meteorological satellites are good at spotting strong storms by how cold their clouds are with respect to the environment: very cold, high cloud tops are only found where strong updrafts push air up to the top of the troposphere. The best approach would be to follow every individual storm and collect its statistics, but that is out of scope for this article (it would mean downloading terabytes of data from EUMETSAT). So I used a derived product that counts overshooting tops, the domes that punch through the top of a storm in the strongest updraft regions. They are a good proxy of how many strong storms formed over an area.

This plot contains a lot of information. Using monthly data I computed the fraction of overshooting-top detections (relative to all classified clouds) over the Mediterranean, split by season (colour). The annotated numbers show each season’s contribution to the yearly total.

If you struggle to find a trend, it’s because there isn’t one: over 2004-2025 the trend is essentially zero (about 0.00 pp/decade, p ≈ 0.9), and in the individual seasons it is either tiny or not statistically significant.

So, over this period, there is no statistically significant increase in strong thunderstorm activity.

Now the caveats:

• This dataset covers only 22 years, much shorter than the other records we looked at.

• The proxy only roughly tracks the number of strong storms. It says nothing about their size or intensity.

Still, looking at the ongoing season, September and October have already exceeded the running average for autumn.

Here is a summary plot putting all the metrics together: more heat, more moisture, more instability, more extreme rain. But not (yet) more thunderstorms.

And here’s another version of the same plot I made while exploring different styles.

Of course you’re free to share it at will.

The following plot shows the Sea Surface Temperature (SST) derived from satellite, averaged over a domain encompassing the entire Mediterranean region. Notably, apart from some short periods in April and May, temperatures this year have been constantly hovering above the 95th percentile (1991–2020).

This is not just a consequence of the exceptional summer, but also derives from the fact that the oceans store excess heat deep down for much longer periods (years). This means that the current anomalies are the result not just of one anomalous season, but of many (i.e. the starting average baseline has moved upwards so we’re always starting the year with excess heat). For this reason, the average summer temperature in the Mediterranean is now consistently hovering around 28°C (rather than 26°C, as it used to be in the past). These are values more representative of tropical oceans than of the mid-latitudes.

Anomalously warm sea surface temperatures usually mean more fuel for thunderstorms to form and intensify, and this started to become apparent towards the end of September, once some bursts of cool air from the Atlantic began reaching the Mediterranean.

This animation shows satellite imagery acquired on October 5th over the western Mediterranean. I combined a composite showing a photographic representation of Earth (GeoColor) with another showing the intensity of thunderstorms through a colour scale ranging from blue to red (cloud-top temperatures).

The dynamics at play here are something we usually observe over tropical oceans: thunderstorms form repeatedly over the same area, interact with one another and intensify again and again, eventually becoming organised into a large system with its own low-pressure centre. In the early morning, some hints of rotation can be seen, suggesting what may be the first stages of tropical cyclogenesis.

This is extremely rare to see in the Mediterranean, as tropical-like structures (Medicanes, or Mediterranean tropical-like cyclones) usually originate from an extratropical disturbance, which then develops some tropical characteristics, such as a central cloud-free eye.

The effects of these storms were clearly felt across France and Spain, where rain fell almost continuously for days. I used radar data (now that the OPERA archive is FINALLY open) to estimate accumulated precipitation from September 29th to October 5th. Here, I’m showing these data in 3D using vertical bars with a height (and colour) proportional to precipitation.

Literally a “mountain” of water has formed over the same region, between Barcelona and Marseille. Note that the values may not be representative of the actual observed precipitation, because I converted radar reflectivity directly into precipitation using the exponential Marshall–Palmer approximation, which may overestimate the totals.

However, I tried to check station data and the totals are not too far from the radar estimation, as shown in this plot.

151 stations recorded more than 300 mm over the week, 523 exceeded 200 mm, and 1,285 exceeded 100 mm, compared with a network-wide median of just 54 mm.

In France, the hardest-hit area was around Gorniès, Saint-Hippolyte-du-Fort, and Thoiras, in the foothills of the Cévennes. Gorniès recorded 416.4 mm, while the official SYNOP station at Saint-Hippolyte recorded 371.8 mm.

Barcelona Airport’s weather station recorded its #7 wettest single day since the station’s digitised record began in 1925, with 135.6 mm on October 3. Fabra, Barcelona’s oldest observatory, had an even more exceptional day in relative terms: 159.1 mm on October 3 makes it the #3 wettest day in its 106-year record. Can Bruixa, a separate weather station within the city of Barcelona, recorded its wettest day since observations began there in 1914, with 213 mm on October 4.

Here is a plot of the cumulative precipitation since the beginning of the year, where the 2026 peak is clearly visible. Note, however, that the current year is not a record: 1963, 1972, 1959, 1951, and 2020 all experienced their own major autumn storm bursts around this same time of year with similar amounts.

Unfortunately, the situation is not going to improve anytime soon. It will take a while for the Mediterranean waters to get rid of the excess heat accumulated over the summer, so we may continue to see thunderstorms and heavy rainfall throughout October if atmospheric instability persists.

Additional Content

Full satellite animation of the beginning of October.

Still frame of the thunderstorms close to Barcelona in the morning of the 4th.

The meteorological summer of 2026 has (finally) come to an end—even though many regions are still experiencing above-average temperatures—and for some, it was probably the most extreme summer they have experienced so far. However, given how global warming is accelerating in recent years, this may indeed not be the worst summer we’ll ever experience in the next 10 or 20 years.

I’ll leave comparisons with previous summers (and the inevitable, mostly useless ranking) for another post. Here, I want to see whether we can actually substantiate the meme with data.

To understand how future summers may change, one of the best tools we can use is climate modelling. Climate models, like weather models, solve the physics governing the atmosphere, ocean and land surface, but they are designed and configured for different purposes and timescales. Weather forecasting tries to predict the actual evolution of the atmosphere over the coming days; climate modelling is primarily concerned with how the statistical characteristics of weather change over months, seasons and decades.

That distinction is important. A climate model does NOT need to reproduce the exact weather sequence of a particular day (or even a particular summer) to tell us something useful about how the climate is changing.

Let’s start with the summer we just had. To quantify how unusual it was, I’ll express the temperature anomaly relative to the 1991–2020 climatological average in units of the climatological standard deviation. This gives us a dimensionless standardized anomaly: essentially, how many σ (sigma) the observed temperature lies above (or below) the reference average1.

There’s no great surprise here: much of Western Europe shows standardized anomalies around 3–4σ. To put that into perspective, under a Gaussian distribution, a positive 3σ anomaly has a probability of about 1 in 740 (0.001%), while a positive 4σ anomaly is about 1 in 31,500 (0.00003%)2.

So how can we take a peek into the future?

Unfortunately, simply taking future summers simulated by a climate model and comparing them directly with 2026 is not very informative. As we said, climate models are designed to reproduce the statistics of the climate, not necessarily the exact characteristics of an individual summer. What we can do instead is take the warming signal simulated by a climate model and apply it to the summer of 2026. In other words, we are asking a very simple question:

This is obviously a highly simplified experiment. We cannot expect to have a summer in the future that is literally identical to 2026, and a more complete analysis would use ensembles of future simulations to capture both the forced warming and changes in variability. Still, this approach is useful for getting an intuitive sense of the scale of the change.

I took the EC-Earth3-CC climate model available through EarthDataHub and calculated the difference between its mean temperature for 2081–2100 and the 1991–2020 climatology under the SSP2-4.5 scenario. The result is shown below.

It’s interesting to note that Europe does not warm uniformly. In this particular model, the Mediterranean region shows some of the strongest warming signals, consistent with the greater warming projected for southern Europe.

We can then take this warming signal and add it to the 2026 summer temperature field to see what a summer with the same temperature pattern, but in a warmer climate, would look like. Here are the results for 2041–2060 and 2081–2100.

Compare these two figures to the first one. Again, there’s nothing particularly surprising here: the patterns already visible in the first map become even more pronounced, especially over the Mediterranean, France and the UK. Some areas reach anomalies above 6σ3.

Here is an animation that directly compares the two.

The following plot shows the evolution of summer temperatures over the full historical and future period simulated by this climate model.

Maybe you’re wondering: why am I showing years going back to 1850, and why would we simulate them in a climate model? The answer gets us to another question that often comes up:

One way to test them is to run historical simulations and compare the simulated climate with observations. Climate models do not reproduce every detail of the observed climate, but CMIP6 models broadly reproduce the observed large-scale evolution of global temperature over the historical period, including the long-term warming trend. This is an important part of model evaluation, but it does not mean that every individual model, or every region, will match observations perfectly.

That is why we use climate models together with observations, multiple models and uncertainty ranges rather than treating any single simulation as a prediction carved in stone.

And that, ultimately, is the point of this exercise: not to claim that we can predict exactly what summer 2045 or 2090 will look like, but to understand how a summer that already feels extraordinary today could become much less extraordinary in a warmer climate.

This analysis was made possible by EarthDataHub data.

Although the cause of the event is not 100% sure at the moment, the trigger seems to be a massive glacier collapse high in the Himalayas.

This morning I was curious to see if we could identify the footprint of this disaster in high-resolution satellite imagery so I started downloading some data. As Sentinel-1 data was not available I resorted to Landsat-8/9 data and filtered for days with low cloud cover. Luckily, I was able to found a Landsat pass from yesterday, just a few hours after the landslide. This is the True Color composite which shows modest cloud coverage.

Unfortunately clouds, snow and ice all look similar (white) to the satellite’s visible eye, so let’s try to use a different composite which targets different channels.

In this second Natural Color composite we can see the extent of the landslide due to the glacier collapse, and even recognize some ice far down the valley. I’m not extremely trained in Landsat imagery and glacier topology so I couldn’t really recognize a defined “scar”, but images from previous years (see final section of this post) seems to suggest yesterday’s image shows a melting that is not consistent with the normal seasonal cycle.

Finding an image from before the collapse was not easy due to the constant presence of clouds in this area, but I was able to identify and process one in mid-June. Here is a GIF with a before/after comparison.

Although it’s still to early to attribute such event to Global Warming, we know increasing temperatures have been contributing to destabilising many glaciers worldwide (especially in the Alps).

Gallery with all composite/images

Armed with patience (and plenty of coffee), I calculated the average surface color 1captured by polar-orbiting satellites over London—one of the regions hit hardest by the dry spell. To keep the focus strictly on land and vegetation, I filtered out ocean pixels and heavy cloud cover.

Here is the result: a grid showing pixel colors every eight days, spanning from 2000 through just a few weeks ago.

Even though my “smart” filtering was not able to get rid of all cloudy days, a seasonal cycle clearly appears in this picture, with the vegetation greening in spring/early summer and dying out in fall/winter. Look closer, though, and the anomalies jump out. Certain summers, including 2026, turn a distinct parched yellow across July and August. The current season is pacing right alongside 2022 and 2018, two of the driest years on record.

If we trade time resolution (moving from 8-day to 16-day windows) for precision, we can leverage a tool built specifically for this job: the Normalized Difference Vegetation Index (NDVI). NDVI monitors plant health by comparing absorbed red light against reflected near-infrared (NIR) light. The map below presents a refined “vegetation calendar” with far less cloud distortion.

The findings are striking: the NDVI data closely mirrors my initial color model, which nicely validates my DIY coding attempt! Due to processing latencies, this dataset only runs through late July, but the trajectory is clear: 2026 is tracking dangerously close to 2018 and 2022.

Here is the twist: rain shortages aren’t the primary driver this time around. Soil moisture data over London reveals drought conditions taking root as early as May.

Cumulative precipitation data shows that total rainfall for 2026 isn’t historically low yet. Current totals sit near the long-term average and well above extreme dryness thresholds, thanks to heavy rains between January and March (which also show up in the soil moisture evolution).

The true culprit shows up when we examine evapotranspiration: this includes both processes of bare soil evaporation and transpiration (through plants), which both contribute to move water from the soil into the atmosphere, thus lowering soil moisture. London (and much of Europe) has endured relentless heatwaves since late May. Extreme temperatures accelerate evaporation from bare soil and also increase plants transpiration (up to a certain tipping point where plants enter a stress-response mode by closing their stomata), rapidly draining soil reserves. This year’s evapotranspiration curve sits well above the 95th percentile, confirming that water is evaporating out of the ground at a historic rate.

You can generate these last plots using my app.

We start from an animation of the cloud cover predicted by ICON over the Eclipse path.

As we can see, most of the path over the Atlantic will be covered by clouds. Only a small portion of Greenland seems to be cloud free. Over Spain the situation seems to be more favorable, however there are few uncertainties related to some thunderstorms forming in the afternoon.

Here is a map that does NOT represent cloud cover directly, but rather the probability (based on ensemble models) of having clear skies (less than 40% cloud cover, which is already pretty generous).

As we can see, there are a few spots in Spain where the probability of having clear skies at this time is quite low (less than 30%). The area with clear skies in the South-West does not unfortunately coincide with the totality path.

Here are the complete galleries with ICON-EU, ICON and ICON-EU-EPS.

What do I mean by that? Let’s start with a video I made back in 2024 of the total eclipse in the United States. It shows an animation of the eclipse shadow moving across the US and its effect on surface weather, as measured every 10 minutes by the ASOS weather network.

Each wind barb shows wind direction and intensity, the numbers show the actual measured temperature, and the colored scatter points show how temperature varies over time, making it easy to spot even small deviations.

As we can see, the umbra passing over the domain causes a local drop in temperature and a decrease in wind speed, both of which recover once the eclipse has moved on. In fact, many studies have shown that the darkening not only causes air cooling but also a decrease in surface wind speeds (as happening over night) and can eventually affect cloud formation1.

Because many weather models don’t account for the reduced radiation caused by an eclipse, they tend to underestimate its effects on surface temperature, wind, and other variables. That’s why scientists have been working to incorporate eclipse effects into forecast models; a parametrization that’s now present in some global models, including ICON.

A few weeks ago I received an email from DWD (the German Weather Service) saying they had finally added a solar eclipse parametrization to ICON, so I decided to check it out myself. Since we’re looking for a fairly small effect, and the model’s hourly output resolution usually isn’t enough to capture such fast, localized changes, I wasn’t expecting much - but once I downloaded and plotted the clear-sky shortwave radiation, I could see a clear signal.

The first animation shows the clear-sky shortwave radiation flux (essentially solar radiation without the effect of clouds). Watch how it evolves over time: as the terminator (the line between darkness and daylight) moves westward across Europe, you can see a brief “dip” near the eclipse’s center line.

These differences are hard to spot with an untrained eye, so I computed the difference between this radiation field and the previous day’s. That’s not a perfectly clean comparison, since some of the difference simply reflects the changing solar angle as we move toward autumn - but the eclipse signal still stands out clearly as a drop in radiation trailing to the southwest of the eclipse’s center line.

Why to the south-west? I think it has to do something with the sun inclination, projecting a shadow on the west side, and on the hourly averaging of radiation coming out from ICON. Furthermore, notice how the darkening is not entirely visible over Greenland, where the albedo of snow and ice is winning. Arguably it would have been better to use the net short-wave radiation at the top of the atmosphere, but this is unfortunately not availabe in the ICON open data sets of data.

As for the effect on surface weather, it will likely be much smaller this time than what was observed in the US two years ago. In this case, the eclipse’s darkening falls over areas with heavy cloud cover, so its effect will be largely masked at the surface. I couldn’t find any noticeable effect in the surface variables I checked for a few locations in Spain - though the hourly output frequency of the models I have makes it hard to spot any meaningful difference in the first place.

The results speak for themselves. Here is a summary of the entire weather station network’s history from 1853 up to today. Every line in this plot represents the daily maximum temperature recorded across the entire network (effectively the hottest station of the day) for a given calendar year.

Not only did this year mark the earliest exceedance of the 40°C threshold on record (40.1°C in Bad Deutsch-Altenburg on June 29th), but the previous all-time national record (40.5°C in Bad Deutsch-Altenburg on August 8, 2013, during one of the worst heatwaves in recent memory) was completely shattered by a new high of 41.2°C in the same town on August 5th.

Even more striking, 17 of the top 20 all-time Austrian temperature records have occurred in 2026, set consecutively day after day. Here is a selection of those top records:

1. 41.2°C – Bad Deutsch-Altenburg (2026-08-05) [Lower Austria, 168.6m]

2. 41.0°C – Wien Stammersdorf (2026-08-04, 2026-08-05) [Vienna, 190.7m]

3. 40.5°C – Bad Deutsch-Altenburg (2013-08-08) [Lower Austria, 168.6m]

4. 40.4°C – Wien Innere Stadt (2026-08-04) [Vienna, 177.0m]

5. 40.3°C – Neusiedl am See (2013-08-08) [Burgenland, 117.0m]

6. 40.2°C – Krems (2026-08-04) [Lower Austria, 202.0m]

7. 40.2°C – Langenlois (2026-08-04) [Lower Austria, 207.0m]

8. 40.1°C – Wieselburg (2026-08-04) [Lower Austria, 258.7m]

The next plot helps put these extreme events into historical context. I compared the number of stations exceeding fixed thresholds (35°C, 37°C, 39°C, and 40°C) across different years. Because the total number of active weather stations in the network has grown steadily over time, I normalized the data by network size to display relative percentages rather than raw counts.

As the plot shows, the 2013 heatwave was the first time stations ever recorded temperatures reaching 40°C (accounting for just 0.002% of total annual network observations). That same year, observations above 35°C hovered around 1%. This year, that share has jumped to over 2%, while the percentage of stations recording maximum temperatures of 40°C or higher has increased by an order of magnitude (0.02%).

As the data clearly shows, most rivers in Central and Eastern Europe are currently experiencing extreme drought conditions. In some cases, such as the Danube and the Rhine, these extreme conditions have persisted for weeks, if not months.

For example, consider the water level of the Rhine measured at Cologne (Köln). By compiling historical data, we can track these levels back to 1981 (though even older data likely exists in non-digitized archives, but I wasn’t able to find them).

On August 1st, the Rhine’s water level at Cologne reached a staggering historic low of just 68 cm. This dropped below the previous record low of 69 cm, which was set in October 2018. This drastic drop threatens to disrupt key sectors of the German economy (such as the chemical and steel industries) which rely heavily on river transport.

Regarding the river’s discharge (essentially the flux of water passing through), historical records dating back to 1816 indicate that while the August values aren’t the absolute lowest ever recorded (those typically occur in winter) they are extremely close, setting a new record low for the month of August.

Similarly historic conditions have been recorded on the Danube at Budapest. These low water levels have had severe consequences for energy infrastructure. In Hungary, the Paks nuclear power plant (which supplies nearly half of the country's electricity) was forced into an unprecedented shutdown of reactor units because the river level dropped 28 cm below its 2018 record low. The water level fell so much that the intake pumps could no longer draw sufficient water to safely cool the plant. Romania's Cernavoda nuclear plant has faced similar drought-related shutdowns.

There is little need to explain the root cause: a profound lack of precipitation. The following map displays the number of precipitation days for June and July. While not a direct measure of precipitation anomaly, it remains highly representative of the drought’s scale.

Most of Western and Southern Europe has seen few, if any, days of precipitation over the past two months. While an arid summer is typical for places like Algiers, it is entirely anomalous for cities like Paris.

Among these events, the wildfire that developed near Bordeaux, France, was probably the most intense. The smoke plume associated with the fire was clearly visible from space for several days, as shown in the image below, which I created by combining both high- and low-resolution channels from the EUMETSAT MTG-FCI instrument.

I chose this particular time because the satellite image appears to show an overshooting top embedded within the plume. Together with evidence from weather radar and ground photographs, this suggests that the fire was intense enough to generate a pyrocumulus cloud—a type of convective cloud that forms above intense heat sources such as large wildfires or volcanic eruptions.

I also found a photograph on Instagram taken from an aircraft in the area, which appears to capture the same cloud and its distinctive overshooting top.

The impact of the fire on the ground is clearly visible in the before-and-after comparison below. Unlike the previous image, this comparison uses Sentinel-2 satellite data, which provides a much higher spatial resolution than the EUMETSAT FCI instrument.

Here, Sentinel-2 imagery has been processed using a custom composite designed to highlight both active fires and recently burned areas.

Overall, the events of the last few weeks have burned an exceptional amount of land. According to data from EFFIS, this is already France’s worst wildfire season in at least 20 years, and by a substantial margin.

So far, approximately 91,000 hectares have burned, the highest total ever recorded by week 30. This is 96% higher than the previous week-30 record, which was set in 2022. For comparison, the entire 2022 wildfire season—previously France’s worst on record—burned around 66,000 hectares.

The situation in Spain is not yet record-breaking, but it is still highly concerning. Current EFFIS data indicate that 2026 ranks as the second-highest week-30 total on record, behind only 2022, when approximately 227,845 hectares had burned by the same point in the year. The 2026 season has already reached about 91% of that pace.

Although the ignition of individual wildfires cannot automatically be attributed to climate change, the exceptional environmental conditions experienced this summer have almost certainly contributed to their intensity and rapid spread.

The persistent combination of below-average rainfall, unusually high temperatures, and intense evapotranspiration has severely depleted soil moisture across much of Western Europe.

The map below shows the Soil Moisture Index Anomaly, which estimates the amount of water stored within the soil and compares it with the long-term climatological average. The anomalies are expressed in standard deviations, making it easier to assess just how unusual the current conditions are.

Across much of Western Europe—including France, southern Germany, and parts of the United Kingdom—soils are exceptionally dry. In some locations, the anomalies exceed six standard deviations below normal, highlighting the extraordinary nature of the ongoing drought.

These extremely dry conditions are also clearly reflected in the vegetation response, as discussed in my previous post.

This is the same view but with the Sandwich product, which enhances the cloud tops associated with deep convection (red shading) and shows some overshooting tops.

When the night sets in, some wave-like patterns can be clearly seen appearing in the upper-left quadrant. These are caused by the outflow boundary associated with the now dying thunderstorms moving over the ocean. In other words, the cold air “dumped” by the thunderstorm’s precipitation flux propagates away from it, bringing strong wind gusts that creates wave-like perturbation that can be seen once they encounter a cloud layer.

Here are the animations in (hopefully) original resolution on Vimeo.

"You cannot connect a single heatwave to global warming; it’s just an individual weather event, not the climate!"

This sentence, which once served as a cautionary principle within the scientific community, has somehow morphed into a standard mantra for climate skeptics worldwide. Today, I want to show you how, in the context of recent warming trends, single heatwaves can indeed be meaningfully linked to global warming.

First and foremost, we need a reliable way to understand how local temperatures have shifted over time. Imagine gathering all daily maximum temperatures recorded at a specific weather station over decades—taking, for instance, the Lindenberg meteorological observatory near Berlin. By plotting these values, we construct a probability distribution that illustrates which temperatures are most common.

In the first figure, the blue curve represents daily maximum temperatures from an arbitrary past baseline (1940–1950)1, while the red curve shows the same distribution for the recent decade (2016–2026). To keep the comparison fair while the current season is still underway, both datasets focus strictly on the period between May 15 and July 19.

Notice how the red curve has shifted to the right. This shift reflects a clear increase in mean temperature, perfectly consistent with global warming theory. For example, comparing the two curves at 24°C reveals a 1.3-fold increase in probability—meaning a 24°C day, which was already well above Lindenberg’s mid-century average, occurs far more frequently today. Conversely, an event with that same likelihood in the older climate would have been roughly 4 degrees cooler.

The crucial bit, however, lies further down the curve—in the tail, where extreme events live. Focusing on the upper-right inset, which zooms in on hot extremes, we can set a threshold like 39°C. In the mid-twentieth century, temperatures this high were virtually impossible2; the theoretical distribution shows a non-zero probability only because of how the curve was fitted, not because such days actually occurred. In recent years, however, temperatures near 39°C have been recorded multiple times just a few years apart. This drives an estimated 1,800-fold3 increase in probability.

There is an important nuance here: even if the absolute change in probability looks small visually, dividing by a baseline probability that was near zero causes the relative ratio to skyrocket4. That sensitivity in the tail is precisely why extreme events show such dramatic relative increases.

This pattern holds across other regions as well. Looking at Paris (Montsouris), the overall distribution shape differs slightly, and the 40°C threshold shows a more modest 16-fold increase. This lower multiplier occurs simply because high-heat events were already somewhat more frequent in Paris during the baseline period.

To expand this analysis across broader regions, we can transition from individual weather stations to reanalysis datasets. While reanalysis carries its own caveats5, it provides spatial consistency across time. For a broader view of Western Europe, we can subtract the long-term mean (climatology) and divide by the standard deviation, converting raw values into standardized temperature anomalies.

In the accompanying animation covering May 15 to July 15 from 1951 through 2026, we compare yearly standardized anomaly distributions against two baselines (1951–1980 and 1991–2020). The contrast is stark: recent years show an unprecedented frequency of temperatures exceeding 4 standard deviations (+4 sigmas).

The lower panel tracks this trend by plotting the annual probability of exceeding the 1951–1980 99th percentile, highlighting a sharp acceleration that takes off around the turn of the century.

Has this acceleration unfolded uniformly across Europe? Not quite—extreme heat has intensified faster in some areas than in others. To visualize this spatial variation, we fit a Generalized Pareto Distribution (a model better suited for extreme tail behavior than a standard Gaussian) to reanalysis temperatures across the continent. By comparing the likelihood of exceeding the local 99th percentile between 1951–1980 and 2016–2026, we map the relative shift in extreme heat risk.

The results highlight Spain, Southern France, and Italy as particularly vulnerable, which aligns with expectation. More alarming, however, is that virtually all of Europe has experienced at least a two-fold increase in the likelihood of 99th-percentile extreme heat events over this timeframe.

Below is a map of the maximum temperatures compiled from the Icelandic Meteorological Office (IMO), which includes data from both official weather stations and the non-official sensors used to monitor road conditions.

While the western half of the country remained relatively mild or outright chilly, the East Fjords were remarkably warm. The four hottest stations all clustered in this eastern region:

• Seyðisfjörður, 27.4°C

• Skjaldþingsstaðir, 26.9°C

• Seyðisfjörður - Vestdalur, 26.8°C

• Neskaupstaður 26.7, °C

Just how unusual are these temperatures? Unfortunately, most of the stations in this top tier lack long-term historical records. Only the Seyðisfjörður - Vestdalur station has a slightly longer dataset, stretching back to 1995.

Using this data, I mapped the distribution of July maximum temperatures for this specific station to see where this event stands.

Historically, this location sees an average daily maximum of around 12°C in July, making the 27°C reading an incredible outlier. In fact, this temperature sits well above the 99.9th percentile, making it an exceptionally rare event for this area.

Looking at Iceland as a whole, the absolute heat records for July are higher still. The all-time national record for the month belongs to Teigarhorn in the East Fjords, which hit 30.5°C on July 22, 1939. Other notable highs include 29.7°C at Þingvellir (July 30, 2008) and 29.5°C at Hjarðarland (July 14, 2025).

While road-monitoring stations sometimes register even higher numbers, they generally aren’t counted in official climatological archives due to local interference. For instance, a road station at Fagradalsfjall registered a scorching 39.6°C on July 28, 2021—though that particular “heatwave” was courtesy of a nearby volcanic eruption!

While most satellite instruments are optimized for daylight, certain wavelengths (like infrared) allow us to see what’s happening in the dark. One of the most fascinating applications of this is tracking how our global night skies are changing over time, using NASA’s Black Marble product.

A recent study revealed that while the planet is generally getting brighter (up 30% globally), the real story lies in the sharp regional contrasts. The animation below shows the change in nighttime lights over about 10 years (2014-2022): dimming areas are indicated with purple, while brightening areas are indicated with yellow.

The video in full resolution can be found here. This animation was created by me based on the data provided in the original study.

Take a close look at Europe. It is one of the regions contributing the most to global dimming. Notice the heavy purple hues blanketed over France, Spain, and the UK. This isn’t economic decline; it’s efficiency. The dimming is primarily driven by the widespread transition to LED lighting and strict EU-level energy efficiency mandates.

Over the United States and Canada, we see a patchwork. While there are distinct pockets of dimming, the overarching trend is still leaning toward brightening, particularly radiating out from major metropolitan hubs.

The view over Asia tells a completely different story. Here, the map shows sweeping, uninterrupted brightening across almost the entire region, reflecting rapid urbanization and infrastructure expansion over the last decade.

Here a radar animation from the PGUA radar site before it went offline (probably due to damage).

Here is a time series of the stations that were close to the storm: notice how some of the stopped transmitting data at the same time as the radar did.

This animation shows the sea surface temperature anomaly from June 6th to June 29th. The colors illustrate deviations from the long-term average: reds indicate temperatures much higher than normal, while blues show cooler-than-average waters. These data are derived directly from satellite observations.

The impact of early summer European heatwaves is clearly visible here. Notice the progressive spike in sea surface temperature anomalies, which exceed an 8°C deviation on June 29th.

In recent years, the Mediterranean Sea has been losing its role as a natural “dampener” that helps mitigate extreme heatwaves. Because water has a much higher heat capacity than air, it warms up far more slowly, acting as a buffer against sudden temperature spikes. However, this buffer only works if heatwaves remain brief and rare, which, unfortunately, has not been the case lately. Instead, consecutive heatwaves have compounded year after year, leaving the Mediterranean little to no time to recover. As a result, like most of the world’s oceans, it has begun trapping heat deep beneath the surface.

The consequences are clearly visible in this final plot, which visualizes heat accumulation by tracking a specific milestone date. I averaged the sea surface temperature across a zone encompassing the central Mediterranean to find the exact day each year when temperatures first crossed the 26.5°C threshold. Why this specific number? It’s an arbitrary threshold that typically represents the absolute peak of an intense summer season.

As the plot highlights, this extreme threshold used to be crossed around mid-August prior to the 2000s. Only during the infamous summer of 2003 did we see it breached as early as mid-July. Yet, even 2003 pales in comparison to recent years: in 2026, the threshold was shattered before July (on June 28th), following a July 2nd breach in 2025. The long-term trend line indicates that this milestone is shifting roughly 10 days earlier per decade. If this continues, by 2040, the Mediterranean could hit this peak summer temperature by mid June.

Unlike the atmosphere, oceans hold onto their energy. Once heat penetrates the deeper layers, cooling them down takes an immense amount of time and effort, and right now, there is absolutely no sign of a reversal.

Here are the raw images at the original resolution (click to enlarge).

Notice how the average color over land transitions from green to light brown, indicating dry soils and less active vegetation. This data is often used to compute indices (such as the Normalized Difference Vegetation Health Index, or NDVI) that allow scientists to track vegetation health over time.

I tried to compute the average of NDVI over France and compare it to the Interquartile Range (2014-2025). Although this product is only updated every 10 days we can indeed see a sharp decrease in June, much anticipated with respect to the climatology.

The exceptional dryness of the soils can be linked to a lack of precipitation during June, which is the same period where the largest temperature anomalies occurred. The map below shows the precipitation anomalies across Europe for the month of June, expressed as a percentage rather than in millimeters. Values around 0% (light green to light yellow) indicate normal conditions, while an anomaly of 100% represents double the expected amount of precipitation.

Many areas in Spain and France, as well as Southern Italy, were characterized by anomalies as low as -80% in June. While not necessarily unprecedented, this deficit is significant enough to cause vegetation stress and drier-than-average soils. Conversely, a few “wet hotspots” are visible, particularly over the UK and Scandinavia.

Luckily, Europe has a deep meteorological memory. Long before computers, dedicated observers were meticulously logging the weather by hand. Famous centennial time series include:

• Uppsala Observatory, Sweden (started in 1722)

• Bologna Observatory, Italy (started in 1723)

• Padova, Italy (started in 1725)

• Prague-Clementinum, Czech Republic (started in 1775)

By digitizing these paper archives and “homogenizing” the data (adjusting for historical changes in thermometer technology and station locations) scientists have created unbroken records spanning centuries. And no, these data were not “corrected to enhance the warming”, no matter what you read online.

One incredible example is the Paris Montsouris station. Established in 1872 and still active today, it has a virtually gap-free record. This dataset is incredibly useful and allows us to contextually compare today’s extreme heatwaves directly with the past.

Take a look at the absolute maximum temperatures recorded every June from 1872 to the present day.

The data reveals three striking trends. First of all, the maximum temperature recorded 2 days ago, on the 24th of June (40.6°C), was the hottest ever measured in June. Second, this didn’t just break the previous June record, it obliterated it. The former record (37.6°C in 1947) stood for nearly 80 years, and the new record beats it by a massive 3°C. Third, looking at the right side of the plot, it's impossible to ignore how almost all of the highest peaks occur in recent years..

In fact, if we compile a leaderboard of the top 10 hottest June days in Paris over the last 154 years, the footprint of climate change becomes starkly clear. Eight out of the top ten days occurred after 2010, and an astonishing six of those days belong to this year alone!

Even more concerning? The record of this year (so far) is only second in the top 10 of the hottest days ever recorded in this time series. The highest value of 42.6°C was recorded on 25 July 2019 in what is usually the hottest time of the year.

Data courtesy of the MeteoFrance archive.

More precisely, cold pools form when rain falls into the air below the storm, which is typically quite moist. As some of the rain evaporates, it cools the surrounding environment through latent heat consumption, while the remaining liquid rainfall survives all the way to the ground, keeping the surface air chilled.

However, data from the weather station at Belle-Île (an island off the western coast of France) tells a completely different story. Looking at the following plot, we can see that the arrival of the thunderstorm’s precipitation this morning was actually marked by strong wind gusts (up to 100 km/h!) and a sudden rise in temperature, spiking from 27°C to around 35°C.

Data from a neighboring weather station nearby shows that this temperature spike was accompanied by a dropping dew point, signaling the sudden presence of extremely dry air, with relative humidity crashing from 70% down to just 27%.

The radar data from that time shows the thunderstorm passing over the island.

So… what exactly is happening here?

As mentioned earlier, normal cold pools require moist air beneath the storm. In this case, however, the air below the thunderstorm was exceptionally dry due to an ongoing heatwave that had pushed maximum temperatures above 40°C across most of Western France. Because the sub-cloud layer was so dry, the falling rain completely evaporated before ever reaching the ground.

This rapid evaporation created a pocket of incredibly dense air. Gravity took over, causing this heavy air pocket to plunge rapidly toward the surface into layers of increasing atmospheric pressure. With no liquid water left to continue evaporating and cooling the air, the air began to warm up rapidly due to compression. This process of compressional heating is highly similar to how Foehn winds warm up as they cascade down the Alps.

An atmospheric sounding from Bordeaux, located relatively close to the island, perfectly captures this setup. It reveals a layer of intensely dry air trapped between the surface and 700 hPa. Notice also how the atmosphere is highly unstable, yet the convection is tightly capped by strong surface inhibition. This is a classic meteorological “loaded gun” scenario, where the right trigger can instantly unleash a violent weather event.

Documented cases of these spectacular “heat bursts” are relatively rare, which makes analyzing anomalies like the Belle-Île event all the more fascinating.

High resolution video.