high-dimensional temporal dependent data arise in a wide range of disciplines. the fact that the classical clt for i.i.d. random vectors may fail in high dimensions makes high-dimensional inference notoriously difficult. more challenges are imposed by temporal and cross-sectional dependence. in this talk, i will introduce the high-dimensional clt for temporal dependent data. its validity depends on the sample size $n$, the dimension $p$, the moment condition and the dependence of the underlying processes. an example is taken to appreciate the optimality of the allowed dimension $p$. equipped with the high-dimensional clt result, we have a new sight on many problems such as inference for covariances of high-dimensional time series which can be applied in the analysis of network connectivity, inference for multiple posterior means in mcmc experiments as well as kolmogorov-smirnov test for high-dimensional dependent data. i will also introduce an estimator for long-run covariance matrices and two resampling methods, i.e., gaussian multiplier resampling and subsampling, to make the high-dimensional clt more applicable. our work is then corroborated by a simulation study with a hierarchical model.