Daily　streamflow　forecasting　is　a　major　determinant　of　ecological　processes　in　running　waters,　healthy　stream　ecology　and　surrounding　environment,　and　accurate　streamflow　forecasting　provides　a　powerful　foundation　for　ecological　assessment,　management,　and　decision-making.　Recently,　data-driven　models　for　different　flow　re-gimes　have　shown　excellent　potential　in　streamflow　forecasting.　However,　the　boundaries　between　different　flow　regimes　were　selected　arbitrarily　without　considering　the　changes　in　boundaries　that　often　occur　over　time　in　the　real　world.　Therefore,　in　this　paper,　an　integrated　modelling　approach　that　couples　a　dynamic　classification　method　with　a　long　short-term　memory　networks　(LSTM)　model　without　data　transformation　(the　DC-LSTM　model)　and　an　LSTM　with　Box-Cox　data　transformation　(the　DC-B-LSTM　model)　is　developed　to　improve　the　performance　of　streamflow　forecasting　considering　different　flow　regimes.　The　boundaries　of　dynamic　classifi-cation　are　dynamic　changing　interval　values　of　related　hydrological　variables　improved　from　traditional　clas-sification　method　just　using　static　single-variable　threshold,　so　dynamic　classification　can　more　fully　explore　the　relationship　and　information　of　hydrological　data.　The　performance　of　both　the　DC-LSTM　and　DC-B-LSTM　models　is　compared　to　that　of　the　LSTM　model　without　data　classification　(the　traditional　LSTM　model)　and　with　data　classification　using　a　traditional　static　method　(the　C-LSTM　model)　based　on　data　from　8　stations　within　4　river　basins　in　different　climate　regions　in　the　United　States.　The　results　show　that　both　the　DC-LSTM　and　DC-B-LSTM　models　out-perform　the　traditional　LSTM　models　(with　or　without　static　data　classification)　for　all　river　basins　considered.　Furthermore,　the　DC-B-LSTM　model　displays　better　performance　than　the　DC-LSTM　model　in　arid　areas.