Regional analysis of ESM models using Bias Corrected spatial disaggregated superresolution convolutional neural networks

Climate change is very crucial for ecological systems and society. But Global climate models run at coarse spatial resolution which is difficult to do regional analysis. Regionalscale projections can be obtained by a technique called statistical downscaling which uses past data to find out the high resolution and low-resolution mapping. There are many methods for statistical downscaling of climate data: 1) Conventional methods 2) Deep learning architecture. Some of the existing works like DeepSd downscaled High-resolution climate projections but in such cases, Global climate model (GCM) data suffers from concept drift, change of mapping between input and label over time. So applying these deep learning models is not a good idea for statistical downscaling. In my study, I have developed new approach of downscaling which outperforms other deep learning architectures like super-resolution convolutional neural network (SRCNN), Long short term memory network (LSTM) in terms of accuracy and reliability. These existing models focus on minimizing the root mean square error (RMSE) and do not take care of the tails or extremes. Therefore the objective function of these models should be changed other than root mean square error (RMSE). My proposed model focuses on both means and extremes. I provide a comparison between proposed and other existing deep learning models in downscaling daily precipitation and temperature from 1.25 to 0.25 resolution over India. I have downscaled 6 Global climate model (GCM) models in our comparative study.