This research examines how the CA-UNET technique can improve hydrological simulations.
The proposed framework enhanced the quality and reliability of CHIRPS rainfall data with a CNN-based approach, yielding Corrected-CHIRPS with superior temporal and spatial correlation relative to the observed dataset.
Applying the CNN-based bias-corrected precipitation dataset within hydrological modeling could aid watershed water resource management and mitigation of floods in data-scarce regions.