  In this paper, we study the Jacobi frame approximation with equispaced
samples and derive an error estimation. We observe numerically that the
approximation accuracy gradually decreases as the extended domain parameter
$\gamma$ increases in the uniform norm, especially for differentiable
functions. In addition, we show that when the indexes of Jacobi polynomials
$\alpha$ and $\beta$ are larger (for example $\max\{\alpha,\beta\} > 10$), it
leads to a divergence behavior on the frame approximation error decay.
