Global Climate Models (GCMs) are important for our understanding of Earth's past, present and future climates. They are based on fundamental physical processes, therefore accurate cloud modelling is important, with clouds strongly influencing the transfer of radiant energy and spatial distribution of latent heat in the atmosphere, influencing weather and climate. The representation of clouds is a major source of uncertainty in climate models and the primary reason behind the inter-model variance in climate response. I will be bringing probabilistic machine learning approaches, such as Gaussian Processes and Generative Adversarial Networks, to this problem. These will be used to reduce computational requirements of GCMs, reduce errors through better understanding of the distribution and uncertainty associated with cloud processes, and improve generalisability for future climate scenarios.