Water　quality　prediction　methods　forecast　the　future　short　or　long-term　trends　of　its　changes,　providing　proactive　advice　for　water　pollution　prevention　and　control.　Existing　water　quality　prediction　methods　only　consider　the　historical　data　of　single-type　or　multi-type　water　quality.　However,　meteorology　and　other　factors　also　have　a　significant　impact　on　water　quality　indicators.　Therefore,　only　considering　the　historical　data　of　water　quality　is　not　feasible.　Unlike　existing　studies,　this　work　proposes　a　hybrid　water　quality　prediction　model　called　CMI　to　solve　the　above　problem.　Before　prediction,　CMI　incorporates　a　multimodal　fusion　mechanism　of　water　quality　time　series　and　remote　sensing　images　of　meteorological　rainfall.　Moreover,　CMI　integrates　the　model　of　ConvNeXt　V2　and　a　multimodal　bottleneck　transformer　to　extract　image　features　for　fusing　the　time　series　and　images.　Furthermore,　it　utilizes　an　emerging　model　of　iTransformer　to　realize　prediction　with　the　fused　features.　Experimental　results　with　real-life　water　quality　time　series　and　remotely　sensed　rainfall　images　demonstrate　that　CMI　outperforms　other　state-of-the-art　fusion　algorithms,　and　the　water　quality　prediction　accuracy　with　fused　meteorological　data　is　13%　higher　on　average　than　that　with　only　water　quality　time　series.　©　2024　IEEE.