In this paper we propose an efficient scalability approach for the trajectory piecewise linear (TPWL) macro models through the utilization of Chebyshev interpolating polynomials in each piecewise region. The scalability achieved is in two dimensions (2D) that mainly improve the local approximation properties of TPWL macro models. Horizontal scalability is achieved by decreasing the number of linearization points along the trajectory, vertical scalability is obtained by extending the range of macro model to predict the response of a nonlinear system for inputs far from training trajectory. In this way more efficient macro models are obtained in terms of simulation speed up of complex nonlinear systems. We provide the implementation details and illustrate the 2D scalability concept with an example using nonlinear transmission line. © 2013 IEEE.