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Sub-seasonal climate forecasting (SSF) focuses on predicting key climatevariables such as temperature and precipitation in the 2-week to 2-month timescales. Skillful SSF would have immense societal value, in areas such asagricultural productivity, water resource management, transportation andaviation systems, and emergency planning for extreme weather events. However,SSF is considered more challenging than either weather prediction or evenseasonal prediction. In this paper, we carefully study a variety of machinelearning (ML) approaches for SSF over the US mainland. Whileatmosphere-land-ocean couplings and the limited amount of good quality datamakes it hard to apply black-box ML naively, we show that with carefullyconstructed feature representations, even linear regression models, e.g.,Lasso, can be made to perform well. Among a broad suite of 10 ML approachesconsidered, gradient boosting performs the best, and deep learning (DL) methodsshow some promise with careful architecture choices. Overall, suitable MLmethods are able to outperform the climatological baseline, i.e., predictionsbased on the 30-year average at a given location and time. Further, based onstudying feature importance, ocean (especially indices based on climaticoscillations such as El Nino) and land (soil moisture) covariates are found tobe predictive, whereas atmospheric covariates are not considered helpful.

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances