With heatwaves among Europe's deadliest climate hazards, a team of scientists led by CMCC has developed a prediction system capable of providing helpful information 4 to 7 weeks before summer, which gives valuable time to improve preparedness.

Trained on data from centuries of climate analysis up to recent years, the machine learning system has demonstrated an increase in forecast efficiency by drastically reducing the computational resources required, making these techniques accessible to a broader number of researchers and institutions.

In many parts of the world, staple crops such as maize and wheat are dependent on rainfall recycled from land rather than oceans, making them more vulnerable to drought. Researchers at Stanford and the University of California San Diego identified a critical threshold in atmospheric moisture sources that could help predict and prevent future crop failures.