  Downscaling is necessary to generate high-resolution observation data to
validate the climate model forecast or monitor rainfall at the micro-regional
level operationally. Dynamical and statistical downscaling models are often
used to get information at high-resolution gridded data over larger domains. As
rainfall variability is dependent on the complex Spatio-temporal process
leading to non-linear or chaotic Spatio-temporal variations, no single
downscaling method can be considered efficient enough. In data with complex
topographies, quasi-periodicities, and non-linearities, deep Learning (DL)
based methods provide an efficient solution in downscaling rainfall data for
regional climate forecasting and real-time rainfall observation data at high
spatial resolutions. In this work, we employed three deep learning-based
algorithms derived from the super-resolution convolutional neural network
(SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to
produce 4x-times high-resolution downscaled rainfall data during the summer
monsoon season. Among the three algorithms, namely SRCNN, stacked SRCNN, and
DeepSD, employed here, the best spatial distribution of rainfall amplitude and
minimum root-mean-square error is produced by DeepSD based downscaling. Hence,
the use of the DeepSD algorithm is advocated for future use. We found that
spatial discontinuity in amplitude and intensity rainfall patterns is the main
obstacle in the downscaling of precipitation. Furthermore, we applied these
methods for model data postprocessing, in particular, ERA5 data. Downscaled
ERA5 rainfall data show a much better distribution of spatial covariance and
temporal variance when compared with observation.
