Due　to　the　inherent　non-stationary　and　nonlinear　characteristics　of　original　streamflow　and　the　complicated　relationship　between　multi-scale　predictors　and　streamflow,　accurate　and　reliable　monthly　streamflow　forecasting　is　quite　difficult.　In　this　paper,　a　multi-scale-variables-driven　stream-flow　forecasting　(MVDSF)　framework　was　proposed　to　improve　the　runoff　forecasting　accuracy　and　provide　more　information　for　decision-making.　This　framework　was　realized　by　integrating　random　forest　(RF)　and　Gaussian　process　regression　(GPR)　with　multi-scale　variables　(hydrometeorological　and　climate　predictors)　as　inputs　and　is　referred　to　as　RF-GPR-MV.　To　validate　the　effectiveness　and　superiority　of　the　RF-GPR-MV　model,　it　was　implemented　for　multi-step-ahead　monthly　streamflow　forecasts　with　horizons　of　1　to　12　months　for　two　key　hydrological　stations　in　the　Jinsha　River　basin,　Southwest　China.　Other　MVDSF　models　based　on　the　Pearson　correlation　coefficient　(PCC)　and　GPR　with/without　multi-scale　variables　or　the　PCC　and　a　backpropagation　neural　network　(BP)　or　general　regression　neural　network　(GRNN),　with　only　previous　streamflow　and　precipitation,　namely,　PCC-GPR-MV,　PCC-GPR-QP,　PCC-BP-QP,　and　PCC-GRNN-QP,　respectively,　were　selected　as　benchmarks.　Experimental　results　indicated　that　the　proposed　model　was　superior　to　the　other　benchmark　models　in　terms　of　the　Nash–Sutcliffe　efficiency　(NSE)　for　almost　all　forecasting　scenarios,　especially　for　forecasting　with　longer　lead　times.　Additionally,　the　results　also　confirmed　that　the　addition　of　large-scale　climate　and　circulation　factors　was　beneficial　for　promoting　the　streamflow　forecasting　ability,　with　an　average　contribution　rate　of　about　15%.　The　RF　in　the　MVDSF　framework　improved　the　forecasting　performance,　with　an　average　contribution　rate　of　about　25%.　This　improvement　was　more　pronounced　when　the　lead　time　exceeded　3　months.　Moreover,　the　proposed　model　could　also　provide　prediction　intervals　(PIs)　to　characterize　forecast　uncertainty,　as　supple-mentary　information　to　further　help　decision　makers　in　relevant　departments　to　avoid　risks　in　water　resources　management.　©　2022　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.