Clouds exert a significant impact on global temperatures and climate change. Cloud-radiative feedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is therefore crucial for accurate climate projections. Biases like the double-ITCZ problem in Global Climate Models (GCMs) hampers precise climate projections. Here, we employ a bias-corrected downscaling method to constrain the cloud feedback uncertainties in the tropical and sub-tropical Atlantic region. We use high-resolution regional climate model (RCM) simulations with resolved deep convection, driven by debiased driving fields from three different global climate models (GCMs). Our simulations project a narrower range of the CRF over the tropical and sub-tropical Atlantic (-0.7 to 0.6 $Wm^{-2}K^{-1}$) compared with the driving GCMs (-0.9 to 1.0 $Wm^{-2}K^{-1}$). Furthermore, using linear regression between equilibrium climate sensitivity (ECS) and the CRF over the studied domain, a slightly narrower range of ECS (3.05 - 4.87 K) is inferred after the downscaling, compared to the GCMs’ result (2.98 - 5.15 K). The narrowed range is mainly due to a reduced range of stratocumulus feedback.   Our study highlights the potential of bias-corrected downscaling in constraining the uncertainty of simulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for further simulations with additional RCMs and domains for a more comprehensive analysis.