  A precise forecast for droughts is of considerable value to scientific
research, agriculture, and water resource management. With emerging
developments of data-driven approaches for hydro-climate modeling, this paper
investigates an AutoML-based framework to forecast droughts in the U.S.
Compared with commonly-used temporal deep learning models, the AutoML model can
achieve comparable performance with less training data and time. As deep
learning models are becoming popular for Earth system modeling, this paper aims
to bring more efforts to AutoML-based methods, and the use of them as benchmark
baselines for more complex deep learning models.
