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Accurately predicting drought a few months in advance is important for drought mitigation and agricultural and water resources management, especially for a river basin like that of the Yellow River in North China. However, summer drought predictability over the Yellow River basin is limited because of the low influence from ENSO and the large interannual variations of the East Asian summer monsoon. To explore the drought predictability from an ensemble prediction perspective, 29-year seasonal hindcasts of soil moisture drought, taken directly from several North American multimodel ensemble (NMME) models with different ensemble sizes, were compared with those produced by combining bias-corrected NMME model predictions and variable infiltration capacity (VIC) land surface hydrological model simulations. It was found that the NMME/VIC approach reduced the root-mean-square error from the best NMME raw products by 48% for summer soil moisture drought prediction at the lead-1 season, and increased the correlation significantly. Within the NMME/VIC framework, the multimodel ensemble mean further reduced the error from the best single model by 6%. Compared with the NMME raw forecasts, NMME/VIC had a higher probabilistic drought forecasting skill in terms of a higher Brier skill score and better reliability and resolution of the ensemble. However, the performance of the multimodel grand ensemble was not necessarily better than any single model ensemble, suggesting the need to optimize the ensemble for a more skillful probabilistic drought forecast.