As the frequency of extreme events across climate and economic sectors rises, it becomes increasingly crucial to understand and detect them at the earliest opportunity. Statistical models provide a way to enhance their interpretability and offer insights into the connections between extreme events. Especially geophysical data is often coupled across both space and time which poses challenges for modeling, often leading to highly complex statistical models. New approaches are needed to estimate causal dependencies between extreme events and to uncover spillover effects. For that we investigate both machine learning methods as well as conventional statistical methods and apply them to model extreme events in the area of finance and climate research.