In　this　study,　we　propose　a　hypothesis　that　an　automatic　calibration　framework　can　address　modeling　uncertainties　in　the　Storm　Water　Management　Model　(SWMM)　due　to　structural　defects　that　result　in　the　inability　of　the　model　to　account　for　runoff　generated　on　building　walls　from　wind-driven　rain.　To　test　this　hypothesis,　we　introduce　a　rainfall　error　model　into　the　calibration　framework　to　indirectly　consider　the　effects　of　inclined　wind-driven　rain　on　building　walls.　We　couple　the　optimization　algorithm　Differential　Evolution　Adaptive　Metropolis　(DREAM)　with　SWMM　using　newly　developed　API　functions.　To　demonstrate　the　effectiveness　of　the　framework,　we　conduct　a　case　study　in　Guangzhou,　China　and　assess　the　impacts　of　rainfall　uncertainty　on　model　parameter　estimations　and　simulated　runoff　boundaries.　The　results　show　that　the　framework　can　improve　the　average　Nash-Sutcliffe　index　of　selected　events　by　more　than　5%.　It　also　captures　peak　flow　more　accurately.　This　framework　contributes　to　the　theory　of　SWMM　calibration　by　accounting　for　structural　defects　and　considering　rainfall　uncertainty.