Studying low-likelihood high-impact extreme weather and climate events in a warming world requires massive ensembles to capture long tails of multi-variate distributions. In combination, it is simply impossible to generate massive ensembles, of say 1000 members, using traditional numerical simulations of climate models at high resolution. We* describe how to bring the power of machine learning (ML) to replace traditional numerical simulations for short week-long hindcasts of massive ensembles, where ML has proven to be successful in terms of accuracy and fidelity, at five orders-of-magnitude lower computational cost than numerical methods. Because the ensembles are reproducible to machine precision, ML also provides a data compression mechanism to avoid storing the data produced from massive ensembles. The machine learning algorithm FourCastNet is based on Fourier Neural Operators (FNO) and Transformers, proven to be efficient and powerful in modeling a wide range of chaotic dynamical systems, including turbulent flows and atmospheric dynamics. FourCastNet has already been proven to be highly scalable on NVIDIA-GPU HPC (High Performance Computing) systems. Until today, generating 1,000- or 10,000-member ensembles of hindcasts was simply impossible because of prohibitive compute and data storage costs. For the first time, we can now generate such massive ensembles using ML at five orders-of-magnitude less computational intensity than traditional numerical simulations. *Authors: William D. Collins, Lawrence Berkeley National Laboratory, U.S.; Karthik Kashinath, NVIDIA; Mike Pritchard, Nvidia Corporation, U.S.; Jaideep Pathak, Lawrence Berkeley National Laboratory, U.S.; Thorsten Kurth, NVIDIA, U.S.; Anima Anandkumar, California Institute of Technology & NVIDIA, U.S.; Travis O’Brien, Indiana University Speakers: William D. Collins University of California at Berkeley Moderators: Duncan Watson-Parris University of California San Diego Philip Stier University of Oxford Join us for two days of never before presented, state of the art AI solutions and cutting edge knowledge, aligned with the UN Sustainable Development Goals. Register and learn more here: https://aiforgood.itu.int/summit23/ Join the Neural Network! 👉https://aiforgood.itu.int/neural-network/ The AI for Good networking community platform powered by AI. Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI. 🔴 Watch the latest #AIforGood videos! https://www.youtube.com/c/AIforGood/videos 📩 Stay updated and join our weekly AI for Good newsletter: http://eepurl.com/gI2kJ5 🗞Check out the latest AI for Good news: https://aiforgood.itu.int/newsroom/ 📱Explore the AI for Good blog: https://aiforgood.itu.int/ai-for-good-blog/ 🌎 Connect on our social media: Website: https://aiforgood.itu.int/ Twitter: https://twitter.com/AIforGood LinkedIn Page: https://www.linkedin.com/company/26511907 LinkedIn Group: https://www.linkedin.com/groups/8567748 Instagram: https://www.instagram.com/aiforgood Facebook: https://www.facebook.com/AIforGood What is AI for Good? We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets. More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals. AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland. Disclaimer: The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.