Weather　data　are　the　key　forces　that　drive　hydrological　processes　so　that　their　accuracy　in　watershed　modeling　is　fundamentally　important.　For　large-scale　watershed　modeling,　weather　data　are　either　generated　by　using　interpolation　methods　or　derived　from　assimilated　datasets.　In　the　present　study,　we　compared　model　performances　of　the　Soil　and　Water　Assessment　Tool　(SWAT),　as　driven　by　interpolation　weather　data,　and　NASA　North　American　Land　Data　Assimilation　System　Phase　Two　(NLDAS2)　weather　dataset　in　the　Upper　Mississippi　River　Basin　(UMRB).　The　SWAT　model　fed　with　different　weather　datasets　were　used　to　simulate　monthly　stream　flow　at　11　United　States　Geological　Survey　(USGS)　monitoring　stations　in　the　UMRB.　Model　performances　were　evaluated　based　on　three　metrics:　coefficient　of　determination　(R-2),　Nash-Sutcliffe　coefficient　(NS),　and　percent　bias　(P-bias).　The　results　show　that,　after　calibration,　the　SWAT　model　compared　well　at　all　monitoring　stations　for　monthly　stream　flow　using　different　weather　datasets　indicating　that　the　SWAT　model　can　adequately　produce　long-term　water　yield　in　UMRB.　The　results　also　show　that　using　NLDAS2　weather　dataset　can　improve　SWAT　prediction　of　monthly　stream　flow　with　less　prediction　uncertainty　in　the　UMRB.　We　concluded　that　NLDAS2　dataset　could　be　used　by　the　SWAT　model　for　large-scale　watersheds　like　UMRB　as　a　surrogate　of　the　interpolation　weather　data.　Further　analyses　results　show　that　NLDAS2　daily　solar　radiation　data　was　about　2.5　MJ　m(-2)　higher　than　the　interpolation　data.　As　such,　the　SWAT　model　driven　by　NLDAS2　dataset　tended　to　underestimate　stream　flow　in　the　UMRB　due　to　the　overestimation　in　evapotranspiration　in　uncalibrated　conditions.　Thus,　the　implication　of　overestimated　solar　radiation　by　NLDAS2　dataset　should　be　considered　before　using　NLDAS2　dataset　to　drive　the　hydrological　model.