Novel statistical downscaling emulator for precipitation projections using deep Convolutional Autoencoder over Northern Africa

This study employed Machine Learning (ML) technique known as Convolutional Autoencoder to build Statistical Downscaling Model (SDM) emulator. Eight General Circulation Models (GCMs) rainfall datasets were selected under the Representative Concentration Pathway (RCP4.5) emission scenario over Northern Africa. Historical rainfall simulation for the period 1951–2005 from 8 GCMs were applied to train/validate the SDM. To evaluate the SDM performance emulating latest Rossby Centre (RCA4) RCM, SDM results were investigated against RCM projection products (2006–2100). Continuous statistics were employed to examine the SDM performance. The SDM has exhibited positive correlation of 0.75 < R