Phillip J. Goodling Jeremy Alejandro Diaz Hayley R. Corson-Dosch Aaron Joseph Heldmyer Scott Douglas Hamshaw Ryan R. McShane Jesse Cleveland Ross Roy Sando Caelan Simeone Erik A. Smith Leah Ellen Staub David Watkins Michael Wieczorek Kendall C. Wnuk Jacob Aaron Zwart John C. Hammond 2026 <p><span id="_mce_caret" data-mce-bogus="1" data-mce-type="format-caret"><span>Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.</span></span></p> application/pdf 10.3389/frwa.2025.1709138 en Frontiers Media Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations article