Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on a 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, water resource management, and emergency planning for droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem and mainly relies on physics-based dynamical models. Meanwhile, recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve their forecasting performance. The SSF dataset and codebase constructed for the work will be made available along with the paper.