Model outputs and model code for machine learning models forecasting streamflow drought across the Conterminous United States

We applied machine learning (ML) models to forecast streamflow drought from 1 to 13 weeks into the future at more than 3,000 streamgage locations across the Conterminous United States. We applied two machine learning methods (long short-term memory [LSTM] neural network; light gradient boosting [LightGBM] machine) and two benchmark model approaches (persistence; autoregressive integrated moving average [ARIMA]) to predict weekly streamflow percentiles with independent models for each forecast horizon. Both ML models were trained using all percentiles (LSTM-all, LightGBM-all) and only percentiles below 30% (LSTM