Reliable　forecast　precipitation　can　support　disaster　prevention　and　mitigation　and　sustainable　socio-economic　development.　Improving　forecast　precipitation　accuracy　remains　a　challenge.　Therefore,　a　novel　method　for　multi-model　forecast　precipitation　integration　considering　long　lead　times　was　proposed　based　on　deep　learning.　First,　the　accuracy　of　numerical　forecast　precipitation　was　evaluated　under　different　lead　times.　Secondly,　an　integrated　model　was　built　by　coupling　the　attention　mechanism　and　a　long　short-term　memory　neural　network　(LSTM).　Finally,　integrated　forecast　precipitation　was　obtained　by　taking　high-precision　numerical　forecast　precipitation　as　an　input　and　examining　its　accuracy　and　applicability.　Considering　the　example　of　the　Yalong　River,　the　results　showed　the　following:　(1)　numerical　forecast　precipitation　fails　to　forecast　precipitation　of　a　＞=　10　mm/d　intensity　well,　and　is　less　applicable　in　streamflow　forecast;　(2)　traditional　machine　learning　methods　for　integrating　multi-model　forecast　precipitation　fail　to　forecast　precipitation　of　a　＞=　25　mm/d　intensity;　(3)　the　LSTM-A　integration　model　formed　by　attention　weighting　after　the　LSTM　output　can　combine　the　advantages　of　numerical　forecast　precipitation　under　different　intensities　and　improve　the　forecast　precipitation　accuracy　for　7-day　lead　times;　and　(4)　the　LSTM-A　integrated　forecast　precipitation　has　the　best　applicability　in　streamflow　forecast,　with　an　NSE　above　0.82　and　an　MRE　below　30%　with　7-day　lead　times.　These　findings　contribute　to　improving　precipitation　forecast　accuracy　at　different　intensities　and　enhancing　defense　against　extreme　weather　events.