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