Weather forecasting is a vitally important tool for tasks ranging from planning day to day activities to disaster response planning. However, modeling weather has proven to be challenging task due to its chaotic and unpredictable nature. Each variable, from temperature to precipitation to wind, all influence the path the environment will take. As a result, all models tend to rapidly lose accuracy as the temporal range of their forecasts increase. Classical forecasting methods use a myriad of physics-based, numerical, and stochastic techniques to predict the change in weather variables over time. However, such forecasts often require a very large amount of data and are extremely computationally expensive. Furthermore, as climate and global weather patterns change, classical models are substantially more difficult and time-consuming to update for changing environments. Fortunately, with recent advances in deep learning and publicly available high quality weather datasets, deploying learning methods for estimating these complex systems has become feasible. The current state-of-the-art deep learning models have comparable accuracy to the industry standard numerical models and are becoming more ubiquitous in practice due to their adaptability. Our group seeks to improve upon existing deep learning based forecasting methods by increasing spatial resolutions of global weather predictions. Specifically, we are interested in performing super resolution (SR) on GraphCast temperature predictions by increasing the global precision from 1 degree of accuracy to 0.5 degrees, which is approximately 111km and 55km respectively.