For　hourly　PM2.5　concentration　prediction,　accurately　capturing　the　data　patterns　of　external　factors　that　affect　PM2.5　concentration　changes,　and　constructing　a　forecasting　model　is　one　of　efficient　means　to　improve　forecasting　accuracy.　In　this　study,　a　novel　hybrid　forecasting　model　based　on　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　and　deep　temporal　convolutional　neural　network　(DeepTCN)　is　developed　to　predict　PM2.5　concentration,　by　modeling　the　data　patterns　of　historical　pollutant　concentrations　data,　meteorological　data,　and　discrete　time　variables’　data.　Taking　PM2.5　concentration　of　Beijing　as　the　sample,　experimental　results　showed　that　the　forecasting　accuracy　of　the　proposed　CEEMDAN-DeepTCN　model　is　verified　to　be　the　highest　when　compared　with　the　statistics-based　models,　traditional　machine　learning　models,　the　popular　deep　learning　models　and　several　existing　hybrid　models.　The　new　model　has　improved　the　capability　to　model　the　PM2.5-related　factor　data　patterns,　and　can　be　used　as　a　promising　tool　for　forecasting　PM2.5　concentrations.　©　2021　Elsevier　B.V.