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Accurate weather forecasts, based on mathematical models derived from physical principles, are computationally expensive. This challenge is exacerbated by the increasing frequency of extreme weather events due to climate change. In contrast to physics-based models, black box deep learning approaches are highly efficient at weather prediction but are difficult to unpack. The aim of this workshop is to create synergy between theory and applications in climate science. The workshop will bring together academic experts and non-academic practitioners in mathematics, machine learning and climate science to discuss new ways on how mathematical and machine learning based approaches can be used in climate science.