The new AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) performance predictions for the anticipated climatic and traffic conditions depend on the values of the numerous input parameters that characterize the pavement materials, layers, design features, and condition. This paper proposes comprehensive local sensitivity analyses (LSA) and global sensitivity analyses (GSA) methodologies to evaluate continuously reinforced concrete pavement (CRCP) performance predictions with MEPDG inputs under various climatic and traffic conditions. A design limit normalized sensitivity index (NSI) was implemented in both LSA and GSA to capture quantitative as well as qualitative sensitivity information. The GSA varied all inputs simultaneously across the entire problem domain while the LSA varied each input independently in turn. Correlations among MEPDG inputs were considered where appropriate in GSA. Two response surface modeling (RSM) approaches, multivariate linear regressions (MVLR) and artificial neural networks (ANN or NN), were developed to model the GSA results for evaluation of MEPDG CRCP input sensitivities across the entire problem domain. The ANN-based RSMs not only provide robust and accurate representations of the complex relationships between MEPDG inputs and distress outputs but also capture the variation of sensitivities across the problem domain. The NSI proposed in LSA and GSA provides practical interpretation of sensitivity relating a given percentage change in a MEPDG input to the corresponding percentage change in predicted distress relative to its design limit value. The "mean plus/minus two standard deviations (μ + 2σ)" GSA-NSI metric (GSA-NSIμ ±2σ) derived from ANN RSM statistics is the best and most robust design input ranking measure since it incorporates both the mean sensitivity and the variability of sensitivity across the problem domain.