The heat has caused weather usage to surge in Europe — except in France, where the signal goes out twice as quickly. Our data shows why: the uncertainty before the storm matters more than the heatwave.
At the end of June, our daily active users in the Czech Republic jumped 310% compared to their average for the previous 14 days. In Italy, +371%. In Germany, +246%. In France, the strongest peak of the month took place much earlier – and the burst at the end of June, very real, was twice as short as elsewhere before falling suddenly.
However, the four countries experienced the same third heat wave in six weeks. If heat alone explained these peaks, France – a market comparable in size of users – should have followed exactly the same curve as its neighbors. That’s not quite what happened. And it is this gap, more subtle than it seems, which deserves to be detailed.
What the data shows
We analyzed 30 days of GA4 data (June 1-30) in the French market, comparing daily active users to the rolling average of the previous 14 days. The beginning of the month concentrates several marked peaks – including one exceeding +350% on June 2 – followed by a pronounced trough in mid-June, when the heat sets in without any real rainy episode to bring it down.
At the end of June, a second surge appears, with two consecutive days exceeding the “major peak” threshold (+80% compared to the rolling average) – a level comparable to what we observe over the same period in Italy, Germany or the Czech Republic. The difference is therefore not in the intensity of the signal, but in its duration. Where the other markets have several days of major peaks towards the end of the month, that of France closes the next day: the very last day of the month already returns to a normal level.
Why it’s probably not a method problem
This difference in duration could easily be explained by statistical noise. But the general shape of the curve – several peaks at the start of the month, a marked trough in the middle, a jump at the end of the month – is identical to the three other markets, which suggests that the underlying mechanism is the same everywhere: it is not the heat which triggers the opening of the application, it is the uncertainty which precedes a rainy episode. In France, this uncertainty seems to have been resolved more quickly at the end of June – the stormy episodes there were as intense as in its neighbors, but less spread out over time.
RainViewer is not a general weather application: it is a rain radar. For most of our users, this isn’t their first weather app — they already have a default app for seven-day forecasts. We are the second, the one that we open in the quarter of an hour before or during a rainy episode, to find out if it is coming, with what intensity, and when it will pass. A stable heat of 37°C does not create this moment of uncertainty — the body already knows this, and it will continue to know this for the next five days. A storm that has not yet broken, yes — and it is precisely this moment that the product serves.
What this means for those who build in weather or climate-tech
The reflex in this sector is to design and communicate around the extreme — the hottest day, the strongest storm. Our data suggests that demand follows volatility, not extremes — and that volatility itself is neither uniform nor of consistent duration across markets, even within the same continental heatwave. In France, the signal was as strong as elsewhere, but significantly shorter.
For a product team, the real question is therefore not only “does my product serve extreme episodes well”, but “does it serve the precise moment – and locally variable, sometimes in duration as well as intensity – when the user cannot know what will happen near him”.
