Soil moisture information is crucial for predicting natural hazards like droughts and landslides, improving climate and weather forecasts, understanding soil-atmosphere exchanges and feedback, and better managing water resources. Soil moisture networks are sparse, and remote sensing products lack necessary resolution. Downscaling techniques aim to enhance resolution, but face challenges because the ancillary data they rely on are often sparse and lack representativeness.<br />Here, a &rdquo;downscaling playground&rdquo; is created over the Contiguous USA using physics-based hydrological simulations that allow downscaling techniques to be trained and tested without practical limitations. We show that existing biases in soil moisture station locations lead to spatial overfitting, resulting in poorer performance than, for example, if those stations had been randomly located. The biases lead also to unwarranted confidence in the improvement achieved with downscaling, as testing is carried out on stations in similar locations. Finally, we propose a new paradigm that fuses physics-based modelling (ParFlow-CLM) with machine learning (XGBoost) and downscales remote sensing observations (e.g., SMAP) to produce a 1km² soil moisture product over CONUS. We test the approach thoroughly against in situ observations.