High-accuracy　streamflow　forecasting　with　long　lead　times　can　help　promote　the　efficient　utilization　of　water　resources.　However,　the　construction　of　cascade　reservoirs　has　allowed　the　evolution　of　natural　continuous　rivers　into　multi-block　rivers.　The　existing　streamflow　forecasting　methods　fail　to　consider　the　impact　of　reservoir　operation.　Thus,　a　novel　short-term　streamflow　forecasting　method　for　multi-block　watersheds　was　proposed　by　integrating　machine　learning　and　hydrological　models.　Firstly,　based　on　IMERG　precipitation,　the　forecast　precipitation　product’s　error　is　corrected　by　the　long　short-term　memory　neural　network　(LSTM).　Secondly,　coupling　convolutional　LSTM　(ConvLSTM)　and　LSTM,　operation　rules　for　cascade　reservoirs　are　extracted.　Thirdly,　a　short-term　deterministic　streamflow　forecasting　model　was　built　for　multi-block　watersheds.　Finally,　according　to　the　sources　of　forecasting　errors,　probabilistic　streamflow　forecasting　models　based　on　the　Gaussian　mixture　model　(GMM)　were　proposed,　and　their　performances　were　compared.　Taking　the　Yalong　River　as　an　example,　the　main　results　are　as　follows:　(1)　Deep　learning　models　(ConvLSTM　and　LSTM)　show　good　performance　in　forecast　precipitation　correction　and　reservoir　operation　rule　extraction,　contributing　to　streamflow　forecasting　accuracy.　(2)　The　proposed　streamflow　deterministic　forecasting　method　has　good　forecasting　performance　with　NSE　above　0.83　for　the　following　1–5　days.　(3)　The　GMM　model,　using　upstream　evolutionary　forecasted　streamflow,　interval　forecasted　streamflow,　and　downstream　forecasted　streamflow　as　the　input–output　combination,　has　good　probabilistic　forecasting　performance　and　can　adequately　characterize　the　＇non-normality＇　and　＇heteroskedasticity＇　of　forecasting　uncertainty.　©　2025　by　the　authors.