<p>This paper presents a novel methodology for integrating climate-related risk drivers into credit risk models, addressing emerging expectations for climate stress testing within financial institutions. The proposed approach transparently translates both physical and transition climate risks into expected loss values, enabling more robust and forward-looking risk assessments. We extend the Asymptotic Single Risk Factor (ASRF) model by calibrating the Z-factor and asset correlation to reflect climate-risk systemic effects. Our framework is empirically validated using four datasets, encompassing the three core credit risk parameters: Probability of Default (PD), Credit Conversion Factor (CCF), and Loss Given Default (LGD). The results demonstrate consistency and interpretability across multi-year forecast horizons, supporting its practical application in long-term climate risk evaluation.</p>