Climate change is one of the most important problems facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to understand the evolution of the climate system subject to various CO2 emission scenarios. Large scale climate simulations produce 100TB-sized spatio-temporal, multi-variate datasets, making it difficult to conduct sophisticated analytics. In this talk, I will present our results in applying Deep Learning for supervised and semi-supervised learning of extreme weather patterns. I will briefly highlight our efforts in scaling Deep Learning to 9000 KNL nodes on a supercomputer. The audience will gain a better appreciation for the breadth of problems for which Deep Learning has been successfully applied, and insights into open challenges for this emerging field.