With scaling technologies, process variations have increased significantly. This has led to deviations in analog performance from their expected values. Performance macromodeling aids in reduction of synthesis time by removing the simulation overhead. In this work, we develop a novel Spline based Center and Range Method (SCRM) for process Variation Aware Performance MACromodeling (VAPMAC) which works on interval valued data. Experiments demonstrate around 200K times computational time advantage using VAPMAC generated macromodels over SPICE montecarlo simulation. The results also demonstrate less than 10% loss in accuracy in computing the performance bounds using the macromodels compared to the spice simulations.