Climate teleconnections are long-range relationships among climate phenomena that underpin the foundation for explaining the climate dynamics of the planet and forecasting extreme events. Although there has been remarkable progress in climate science, many teleconnections remain inadequately characterized or even unidentified, partly due to the limitations of conventional statistical methods that cannot capture complex spatio-temporal dependencies and causality. A machine learning model can address these limitations. One approach to representing spatio-temporal relationships in climate data is through graphs, where nodes are points in space and time projected onto a spherical approximation of Earth, and edges connect each node at a specific time step to its neighbors in the subsequent time step.