Conventional pixel-domain block matching temporal (inter) prediction is suboptimal, since it ignores the underlying spatial correlation. Hence in our recent research we proposed transform domain temporal prediction (TDTP), wherein spatially de-correlated transform coefficients are individually predicted. Later we proposed extended block TDTP (EB-TDTP), which fully exploits spatial correlation around reference block boundaries. However, the transform domain temporal correlation exploited by (EB-)TDTP interferes with the frequency response of sub-pixel interpolation filters. Thus, in this paper, we propose to replace the standard sub-pixel interpolation with filters which are jointly designed with EB-TDTP based on statistics of the data, for either separable or non-separable interpolation structures. We also employ a two-loop asymptotic closed-loop (ACL) approach for statistically stable off-line design. Experiments show that our framework can achieve up to 1dB gain in PSNR over HEVC.