Fuzzy　neural　network　(FNN)　is　regarded　as　a　prominent　approach　in　application　of　time-series　modeling.　With　the　capability　of　fuzzy　reasoning,　FNN　can　capture　temporal　patterns　from　the　time-series　samples.　However,　the　existing　FNNs　may　suffer　from　the　temporal　pattern　distortion　because　possibly　multiscale　features　cannot　be　explored　sufficiently.　To　address　this　problem,　a　time-aware　fuzzy　neural　network,　based　on　the　frequency-enhanced　modulation　mechanism　(FEM-TAFNN),　is　developed　for　time-series　prediction　in　this　article.　First,　a　Fourier-based　decoder　is　established　to　extract　the　multiscale　features.　This　decoder　employs　the　frequency-domain　model　to　orthogonally　separate　the　time-scale　features　with　different　frequencies　into　independent　temporal　patterns　based　on　the　Fourier　basis,　which　prevents　the　overlap　of　temporal　patterns　using　time-domain　analysis.　Second,　a　frequency-enhanced　modulation　mechanism　is　designed　to　shape　fuzzy　rules　of　FNN　based　on　the　contribution　of　different　temporal　patterns　in　the　frequency　spectrum.　It　enables　FEM-TAFNN　to　modulate　out　the　realistic　multiscale　temporal　patterns.　Finally,　the　proposed　FEM-TAFNN　is　tested　on　four　multiscale　time-series　datasets.　The　empirical　results　confirm　its　superior　prediction　performance　than　other　methods.