EGU24-2552, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Downscaling and reconstruction of high-resolution precipitation fields using a deep residual neural network: An assessment over Indian subcontinent

Midhun Murukesh and Pankaj Kumar
Midhun Murukesh and Pankaj Kumar
  • Indian Institute of Science Education and Research Bhopal, Department of Earth and Environmental Sciences, Bhopal, India (

Deep learning methods have emerged as a potential alternative for the complex problem of climate data downscaling. Precipitation downscaling is challenging due to its stochasticity, skewness, and sparse extreme values. Also, the extreme values are essential to preserve during downscaling and extrapolating future climate projections, as they serve as trivial signals for impact assessments. This research looks into the usefulness of a deep learning method designed for gridded precipitation downscaling, focusing on how well it can generalize and transfer what it learns. This study configures and evaluates a deep learning-based super-resolution neural network called the Super-Resolution Deep Residual Network (SRDRN). Several synthetic experiments are designed to assess its performance over four geographically and climatologically distinct domain boxes over the Indian subcontinent. Domain boxes over Central India (CI), Southern Peninsula (SP), Northwest (NW), and Northeast (NE), exhibiting diverse geographical and climatological characteristics, are chosen to assess the generalization and transferability of SRDRN. Following the training on a set of samples from CI, SP and NW, the performance of the models is evaluated in comparison to the Bias Correction and Spatial Disaggregation (BCSD), a renowned statistical downscaling method. NE is a transfer domain where the trained SRDRN models are directly applied without additional training or fine-tuning. Several objective evaluation metrics, like the Kling-Gupta Efficiency (KGE) score, root mean squared error, mean absolute relative error, and percentage bias, are chosen for the evaluation of SRDRN. The systematic assessment of SRDRN models (KGE~0.9) across these distinct regions reveals a substantial superiority of SRDRN over the BCSD method (KGE~0.7) in downscaling and reconstructing precipitation rates during the test period, along with preserving extreme values with high precision. In conclusion, SRDRN proves to be a promising alternative for the statistical downscaling of gridded precipitation.

Keywords: Precipitation, Statistical downscaling, Deep learning, Transfer learning, SRDRN

How to cite: Murukesh, M. and Kumar, P.: Downscaling and reconstruction of high-resolution precipitation fields using a deep residual neural network: An assessment over Indian subcontinent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2552,, 2024.