Artificial intelligence has been transformative for earth and environmental sciences: nowadays this technique is a common instrument scientists’ toolbox. In the domain of meteorology, machine learning often displays superior accuracy compared to traditional computational methods. Even in weather prediction, where complex numerical PDE-solving codes have seen decades of development, graph neural networks and transformer architectures have proven to produce more skillful forecast at a fraction of the computational cost. Inspired by the recent developments in generative modeling of textual data through large language models, several research groups have made efforts to design a foundation model for weather and climate, one that allows fine-tuning for specific objectives and benefits from a pre-trained rich latent space. The WeatherGenerator EU project aims to develop the leading European AI foundation model of the atmosphere. This model will be pre-trained with petabytes of multi-modal data (reanalyses, station observations, satellite products,…) on Europe’s first exascale-class supercomputers, ultimately keeping Europe’s global forecast capabilities at the forefront as we enter an era of democratized data-driven weather prediction.