Short-term　intensive　rainfall　(3-h　rainfall　amount　＞　30　mm)　is　a　destructive　weather　phenomenon　that　is　poorly　predicted　using　traditional　forecasting　methods.　In　this　study,　we　propose　a　model　using　European　Center　for　Medium-Range　Weather　Forecasts　(ECMWF)　data　and　a　machine　learning　framework　to　improve　the　ability　of　short-term　intensive　rainfall　forecasting　in　Fujian　Province,　China.　ECMWF　forecast　data　and　ground　observation　station　data　(2015–2018)　were　interpolated　using　a　radial　basis　function,　outliers　were　processed,　and　the　data　were　blocked　according　to　the　monthly　cumulative　rainfall　and　forecast　window.　Subsequently,　the　box　difference　index　was　used　to　select　features　for　each　data　block.　As　short-term　intensive　rainfall　events　are　rare,　a　data　processing　method　based　on　the　K-means　and　generative　adversarial　nets　was　used　to　address　data　imbalances　in　the　rainfall　distribution.　Finally,　focal　loss　object　detection　was　combined　with　a　deep　belief　network　to　construct　the　short-term　intensive　rainfall　classification　model.　The　results　show　that　the　data　preprocessing　method　and　resampling　method　used　in　this　study　were　effective.　Furthermore,　the　classification　model　was　superior　to　other　machine　learning　methods　for　predicting　short-term　intensive　rainfall.　©　2020　Elsevier　B.V.