Figs. \[h\]–\[parameters\], we show the two-dimensional posterior distribution contours of constraint results in the $\Lambda$CDM, $w$CDM, CPL and HDE models at the 68$\%$ and 95$\%$ CL.

From these figures, we clearly see that when the E-ELT mock data are combined with SN+CMB+BAO, the parameter spaces can be significantly reduced in the $\Lambda$CDM, $w$CDM, and HDE models, while there is little significant improvement in the parameter space for the CPL model. Adding the SKA1 mock data to the data combination of SN+CMB+BAO+E-ELT, the parameter spaces are sharply reduced. In particular, when the SKA2 mock data are combined with SN+CMB+BAO+E-ELT, the improvement is actually much more significant than the case of SN+CMB+BAO+E-ELT+SKA1. Meanwhile, from Fig. \[parameters\], we can easily find that the E-ELT and SKA mock data can help to break the parameter degeneracies, in particular between the parameters $\Omega_{\rm m}$ and $c$ in the HDE model.

From Table \[Precision\], we can easily find that the E-ELT, SKA1, and SKA2 can significantly improve the constraints on almost all the parameters to different extent, in particular for SKA2. Concretely, when the E-ELT mock data are combined with SN+CMB+BAO, the precision of $\Omega_{m}$ is improved from $1.19\%$ to $1.07\%$ in the $\Lambda$CDM model, from 