Global Prediction of Flash Drought Using Machine Learning

Abstract

Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning-based framework that uses meteorological forecasts as inputs to predict global root-zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state-of-the-art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub-seasonal scales.