In this project we will develop multiple dimensionality reduction methods for climate and weather data using machine learning techniques. Examples of such methods include but not restricted to Auto-Encoders, Variational Auto-Encoders, Convolutional Auto-Encoders, and Diffusion Maps. The output from these methods will then be used to build emulators which in turn will be used to enhance the forecasts of climate and weather models.