Earth’s climate is chaotic and noisy. Finding usable signals amidst all of the noise can be challenging. Here, I will demonstrate how explainable artificial intelligence (XAI) techniques can sift through vast amounts of climate data and push the bounds of scientific discovery. But machine learning models are only as capable as the scientists designing them, and climate science requires the crafting of domain specific XAI methods, both to gauge the trustworthiness of the XAI’s predictions and quantify uncertainty, but also to uncover predictable signals we didn't know were there.