Daily　groundwater　level　is　an　indicator　of　groundwater　resources.　Accurate　and　reliable　groundwater　level　(GWL)　prediction　is　crucial　for　groundwater　resources　management　and　land　subsidence　risk　assessment.　In　this　study,　a　representative　deep　learning　model,　long　short-term　memory　(LSTM),　is　adopted　to　predict　groundwater　level　with　the　selected　predictors　by　partial　mutual　information　(PMI),　and　bootstrap　is　employed　to　generate　different　samples　combination　for　training　many　LSTM　models,　and　the　predicted　values　by　many　LSTM　models　are　used　for　the　uncertainty　assessment　of　groundwater　level　prediction.　Two　wells　of　different　climate　zones　in　the　USA　were　used　as　a　case　study.　Different　significant　predictors　of　GWL　for　two　wells　were　identified　by　PMI　from　candidate　predictors　incorporating　teleconnection　patterns　information.　The　results　show　that　GWL　is　significantly　affected　by　antecedent　GWL,　AO,　Nino　3.4,　Nino　1　+　2,　and　precipitation　in　humid　areas,　and　by　antecedent　GWL,　AO,　Nino　3.4,　Nino　3,　Nino　1　+　2,　and　PNA　in　arid　areas.　Predictor　selection　can　assist　in　improving　the　prediction　performance　of　the　LSTM　model.　The　relationship　between　GWL　and　significant　predictors　were　modeled　by　the　LSTM　model,　and　it　achieved　higher　accuracy　in　humid　areas,　while　the　performance　in　arid　areas　was　poorer　due　to　limited　precipitation　information.　The　performance　of　LSTM　was　improved　by　increasing　correlation　coefficient　(R-2)　values　by　10%　and　25%　for　2　wells　compared　to　generalized　regression　neural　network　(GRNN).　Three　uncertainty　evaluation　metrics　indicate　that　LSTM　reduced　the　uncertainty　compared　to　GRNN　model.　LSTM　coupling　with　PMI　and　bootstrap　can　be　a　promising　approach　for　accurate　and　reliable　groundwater　level　prediction　for　different　climate　zones.