Reliable damage forecasting from droughts, which mainly stem from a spatiotemporal imbalance in rainfall, is critical for decision makers to formulate adaptive measures. The requirements of drought forecasting for decision makers are as follows: (1) the forecast should be useful for identifying both the afflicted areas and their severity, (2) the severity should be expressed quantitatively rather than statistically, and (3) the forecast should be conducted within a short time and with limited information. To satisfy these requirements, this study developed a drought forecasting method that sequentially involves the water balance model and a deep neural network (DNN). The annual water shortage in the study area was estimated with the former, and meteorological data and the annual water shortage data were used as independent and dependent variables, respectively, for the latter model’s training. The results from the water balance analysis were more reliable for identifying the four severely impacted areas based on the amount of water shortage, while the meteorological drought index indicated that the 20 sub-basins were severely influenced in the worst year of the drought. For the DNN model’s training, representative concentration pathway scenarios (RCP scenarios) were adopted as future events to extend the available data for the model training. Compared to the model trained with a limited number of past observed data (correlation coefficient = 0.52~0.63), the model trained with the RCP scenarios exhibited a significant increase in the correlation coefficient of 0.82~0.83. Additionally, the trained model afforded reliable drought damage forecasting with various meteorological conditions for the next several months. The trained short-term forecasting model can help decision makers promptly and reliably estimate the damage from droughts and commence relief measures well before their onset.