  Sub-seasonal climate forecasting (SSF) is the prediction of key climate
variables such as temperature and precipitation on the 2-week to 2-month time
horizon. Skillful SSF would have substantial societal value in areas such as
agricultural productivity, hydrology and water resource management, and
emergency planning for extreme events such as droughts and wildfires. Despite
its societal importance, SSF has stayed a challenging problem compared to both
short-term weather forecasting and long-term seasonal forecasting. Recent
studies have shown the potential of machine learning (ML) models to advance
SSF. In this paper, for the first time, we perform a fine-grained comparison of
a suite of modern ML models with start-of-the-art physics-based dynamical
models from the Subseasonal Experiment (SubX) project for SSF in the western
contiguous United States. Additionally, we explore mechanisms to enhance the ML
models by using forecasts from dynamical models. Empirical results illustrate
that, on average, ML models outperform dynamical models while the ML models
tend to be conservatives in their forecasts compared to the SubX models.
Further, we illustrate that ML models make forecasting errors under extreme
weather conditions, e.g., cold waves due to the polar vortex, highlighting the
need for separate models for extreme events. Finally, we show that suitably
incorporating dynamical model forecasts as inputs to ML models can
substantially improve the forecasting performance of the ML models. The SSF
dataset constructed for the work, dynamical model predictions, and code for the
ML models are released along with the paper for the benefit of the broader
machine learning community.
