Scientists have for the first time linked human-induced climate change and global daily weather patterns in a new study.

The report, released Thursday in Nature Climate Change, could mark a transformation in long-held beliefs about the separation between daily weather and long-term climate change.

The study also suggests that measurements analyzing humankind’s role in producing incidents such as heat waves and floods could underestimate the contribution people make to such extreme weather events.

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The study concludes that patterns of global temperature and humidity have human factors and are distinct from natural variability. It also determines that the long-term rise in global average temperature can be predicted with one day’s weather information worldwide.

“We’ve always said when you look at weather, that’s not the same as climate,” study co-author Reto Knutti told The Washington Post. “That’s still true locally; if you are in one particular place and you only know the weather right now, right here, there isn’t much you can say.”

“Global mean temperature on a single day is already quite a bit shifted. You can see this human fingerprint in any single moment,” he added. “Weather is climate change if you look over the whole globe.”

The study, conducted by researchers in Switzerland and Norway, tapped machine learning to measure the correlation between patterns of temperature and moisture at daily, monthly and yearly time scales and metrics of global average surface temperatures and the energy imbalance of the planet. The researchers then analyzed human impact on climate change from those results.

“This ... is telling us that anthropogenic climate change has become so large that it exceeds even daily weather variability at the global scale,” Michael Wehner of Lawrence Berkeley National Laboratory told the Post. “This is disturbing as the Earth is on track for significantly more warming in even the most optimistic future scenarios.”

However, the study does contain uncertainties, including the accuracy of computer models in simulating various climate cycles and the use of machine learning techniques. It also does not incorporate other factors that influence the climate such as human-made and volcanic aerosols.