The challenges of climate change, even under the milder scenarios, require urgent adaptation and mitigation strategies. Whilequantifying the potential impacts of climate change involves modeling work, providing decision-relevant evidence is challengingdue to many sources of uncertainty involved in the estimation process. In this study, we examined regional-scale impact ofclimate change on maize and wheat in Ethiopia using three well calibrated and validated process-based crop models. Thecrop models were driven by gridded and high-resolution climate projections from nine global climate models (GCMs) underthree emission scenarios with CO2 fertilization effect considered. The large ensemble of model simulations allows us tocomprehensively quantify the uncertainties in the model estimated climate change impact, thereby increasing the confidence inthe simulations. Our results show that the national projected median and 5th percentile wheat yield was reduced by 4% and18%, respectively by the end of the 21th century under the high-emission scenario (SSP5-8.5). In contrast, national maizemedian yield increased by 2.5%, with the 5th percentile yield projected to decrease by up to 4%. The CO2 fertilization isexpected to have a compensatory effect on the projected wheat (up to 17%) and maize (up to 12%) yield changes. The largestcontributors to overall yield change uncertainty was identified to be the spread in the crop models followed by GCM spread. Weconcluded that by understanding the sources of uncertainty and using techniques to quantify and manage it, it is possible toimprove the accuracy of model predictions and make better decisions in the face of climate change.