DescriptionAccurately and reliably predicting weather and climate change and associated extreme weather events are critical to plan for disastrous impacts well in advance and to adapt to sea level rise, ecosystem shifts, and food and water security needs. The ever-growing demands of high-resolution weather and climate modeling require exascale systems. Simultaneously, petabytes of weather and climate data are produced from models and observations each year. Artificial Intelligence (AI) offers novel ways to learn predictive models from complex datasets, at scale, that can benefit every step of the workflow in weather and climate modeling: from data assimilation to process emulation to numerical simulation acceleration to ensemble prediction. Further, how do we make the best use of AI to build or improve Earth digital twins for a wide range of applications from extreme weather to renewable energy, including at highly localized scales such as cities? The next generation of breakthroughs will require a true nexus of HPC and large-scale AI bringing many challenges and opportunities. This mini-symposium will delve into the challenges and opportunities at the nexus of HPC and AI. Presenters will describe scientific and computing challenges and the development of efficient and scalable AI solutions for weather and climate modeling.