Study region: The Paskouhak representative basin in southwestern Iran, lies in elevations ranging from 2007 to 2931 m, and a semi-arid climate, the basin drains into Maharlou Lake. Rainfall-runoff records from 2011 to 2023, including training events (2011-2020) and validation events (2021-2023), were analyzed. Study focus: This study evaluates and refines the USDA Soil Conservation Service Curve Number (SCS-CN) method by calibrating both the CN and initial abstraction coefficient (lambda) using observed rainfall-runoff data. Tabulated CN values (USDA lookup) were compared against event-based CN estimates across three antecedent moisture classes. Four lambda adjustment approaches- constant (lambda= 0.2), discrete (mean/median), rainfall-dependent regression, and simultaneous model fittingwere applied. Model performance was assessed on the validation dataset using Nash-Sutcliffe Efficiency (NSE), root-mean-square error (RMSE), and mean absolute error (MAE). New hydrological insights for the region: Calculated CN values were consistently lower than standard tables, reflecting local soil-land use conditions, and simultaneous model fitting (CN = 59.4; lambda = 0.018) delivered the best performance (NSE = 0.78; RMSE = 1.68 mm; MAE = 1.30 mm) compared to conventional settings. A targeted Monte Carlo analysis of this optimal scenario confirmed that keeping parameter uncertainty within +/- 5 % preserves high skill (mean NSE approximate to 0.76; 95 % CI (Confidence Interval): 0.70-0.78), whereas larger perturbations degrade accuracy. These findings underscore the importance of site-specific, joint calibration of CN and lambda with tightly constrained uncertainty for dependable runoff prediction in semi-arid basins.