Abstract
In this chapter, we cover the current state of machine learning (ML) applied to subseasonal-to-seasonal (S2S) prediction and predictability. This includes ML applications for postprocessing and online bias correction, data-driven forecasting, and scientific discovery. We detail best practices, community efforts, as well as previous research and future directions for ML applications to S2S prediction and predictability.