To　accurately　acquire　the　spatial　distribution　and　resources　of　precipitation　in　small　mountainous　watersheds,　this　study　employed　the　Kriging　interpolation　method　for　spatial　downscaling　of　low-　resolution　satellite　data.　It　integrated　local　satellite　and　observational　data　using　the　long　short-　term　memory　(LSTM)　network,　enhancing　the　temporal　correlation　between　satellite　and　observed　precipitation　by　incorporating　antecedent　precipitation　information.　This　model　was　further　utilized　to　estimate　the　spatial　distribution　of　watershed　precipitation.　The　results　indicate　that,　spatially,　the　fusion　model　captures　the　location　of　rainstorm　centers　with　greater　precision.　In　terms　of　precipitation　amount,　the　proposed　model　shows　a　probability　of　detection　and　a　critical　success　index　of　0.　60　and　0.　50,　respectively,　under　short-　duration　intense　rainfall,　improving　the　original　low-resolution　satellite　rainfall　data　to　better　approximate　actual　conditions.　As　for　the　number　of　precipitation　stations,　the　accuracy　of　the　merged　precipitation　data　increases　with　the　number　of　stations,　reaching　stability　when　a　critical　value　of　station　density　is　achieved.　The　Kriging-　LSTM　model　offers　a　novel　approach　for　precisely　acquiring　precipitation　resources　in　small　mountainous　watersheds.　©　2024　International　Research　and　Training　Center　on　Erosion　and　Sedimentation　and　China　Water　and　Power　Press.　All　rights　reserved.