  Deep Learning (DL) based downscaling has become a popular tool in earth
sciences recently. Increasingly, different DL approaches are being adopted to
downscale coarser precipitation data and generate more accurate and reliable
estimates at local (~few km or even smaller) scales. Despite several studies
adopting dynamical or statistical downscaling of precipitation, the accuracy is
limited by the availability of ground truth. A key challenge to gauge the
accuracy of such methods is to compare the downscaled data to point-scale
observations which are often unavailable at such small scales. In this work, we
carry out the DL-based downscaling to estimate the local precipitation data
from the India Meteorological Department (IMD), which was created by
approximating the value from station location to a grid point. To test the
efficacy of different DL approaches, we apply four different methods of
downscaling and evaluate their performance. The considered approaches are (i)
Deep Statistical Downscaling (DeepSD), augmented Convolutional Long Short Term
Memory (ConvLSTM), fully convolutional network (U-NET), and Super-Resolution
Generative Adversarial Network (SR-GAN). A custom VGG network, used in the
SR-GAN, is developed in this work using precipitation data. The results
indicate that SR-GAN is the best method for precipitation data downscaling. The
downscaled data is validated with precipitation values at IMD station. This DL
method offers a promising alternative to statistical downscaling.
