Accurate　short-term　forecasting　of　intensive　rainfall　has　high　practical　value　but　remains　difficult　to　achieve.　Based　on　deep　learning　and　spatial–temporal　sequence　predictions,　this　paper　proposes　a　hierarchical　dynamic　graph　network.　To　fully　model　the　correlations　among　data,　the　model　uses　a　dynamically　constructed　graph　convolution　operator　to　model　the　spatial　correlation,　a　recurrent　structure　to　model　the　time　correlation,　and　a　hierarchical　architecture　built　with　graph　pooling　to　extract　and　fuse　multi-level　feature　spaces.　Experiments　on　two　datasets,　based　on　the　measured　cumulative　rainfall　data　at　a　ground　station　in　Fujian　Province,　China,　and　the　corresponding　numerical　weather　grid　product,　show　that　this　method　can　model　various　correlations　among　data　more　effectively　than　the　baseline　methods,　achieving　further　improvements　owing　to　reversed　sequence　enhancement　and　low-rainfall　sequence　removal.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.