  Deep learning has been utilized for the statistical downscaling of climate
data. Specifically, a two-dimensional (2D) convolutional neural network (CNN)
has been successfully applied to precipitation estimation. This study
implements a three-dimensional (3D) CNN to estimate watershed-scale daily
precipitation from 3D atmospheric data and compares the results with those for
a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the
vertical direction (3D-CNN-Vert). The precipitation estimates of these extended
CNNs are compared with those of the 2D CNN in terms of the root-mean-square
error (RMSE), Nash-Sutcliffe efficiency (NSE), and 99th percentile RMSE. It is
found that both 3D-CNN-Time and 3D-CNN-Vert improve the model accuracy for
precipitation estimation compared to the 2D CNN. 3D-CNN-Vert provided the best
estimates during the training and test periods in terms of RMSE and NSE.
