Soil water availability is a critical constraint to agricultural productivity. While soil water forecasting has previously been conducted in the literature for irrigated fields and surface soil (0–10 cm), there is a limited understanding of subsurface soil water in dryland paddocks, particularly in Australia. Due to rooting depth typically extending below 10 cm, subsurface soil water forecasts in cropping systems, for example, enable decision-making relating to sowing, fertiliser use, and seasonal yield potential. This study aimed to identify the best performing methods, and the underlying variables that affect accuracy when forecasting subsurface soil water (30–100 cm). The methods appraised in this study are Random Forest, XGBoost, Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and an Encoder-decoder LSTM (ENCDEC). Various in situ and remotely sensed meteorological data were used to forecast soil water up to multiple months ahead, at 54 probe sites across Australia.</p> <p>We find all models can perform with relatively similar accuracy for sub-monthly forecasts. For longer lead times, MLP and XGBoost performed better for sites with uniform rainfall throughout the year, and the memory-based models, LSTM and ENCDEC, better at sites with seasonally-dominant rainfall. Furthermore, we determine an upper limit to model utility, as accuracy can become poor many months ahead, and can be replaced by a historic average as an estimate. We find our models to be 'useful' up to 4 months ahead on average, after which accuracy is too low, or a historic average outperforms a model. We find rainfall intensity and seasonality, and model choice, to be key drivers of forecast accuracy at a new site. This study highlights the capability of elementary forms of each ensemble and deep learning model to provide sufficiently accurate soil water forecasts, particularly in the absence of a rainfall forecast feature.