Land　surface　temperature　and　moisture　are　central　components　of　the　Earth＇s　surface　heat　budget.　China　has　experienced　substantial　land　use/cover　change　that　has　led　to　deterioration　of　the　urban　microclimate,　thus　affecting　global　climate　change.　Understanding　the　spatial　non-stationarity　in　the　relationships　between　climate　and　land　cover　across　a　highly　heterogeneous　surface　of　urban　landscapes　is　important　for　improving　urban　planning　and　management.　This　study　used　Landsat-8　OLI/TIRS　data　to　explore　the　relationship　of　the　three　components　(index-based　built-up　index　(IBI);　bare　soil　index　(SI);　and　normalized　difference　vegetation　index　(NDVI))　with　the　urban　climate　(land　surface　temperature　(LST)　and　land　surface　moisture　(LSM))　using　both　a　global　model　(ordinary　least　squares　(OLS))　and　a　local　model　(geographically　weighted　regression　(GWR))　for　a　megacity　in　Southeast　China.　The　global　regression　results　showed　that　there　were　significant　positive　correlations　between　the　LST　and　the　IBI　and　SI,　while　significant　negative　correlations　were　observed　between　the　LST　and　the　NDVI;　opposite　results　were　observed　for　the　LSM.　The　IBI　is　the　factor　having　the　greatest　impact　on　the　LST,　while　the　SI　is　among　the　most　important　factors　for　the　LSM.　The　local　regression　results　showed　that　the　response　of　urban　climate　to　land　surface　is　affected　greatly　by　water　areas,　but　the　role　of　the　water　areas　is　impacted　by　their　size　and　surrounding　landscape　patterns.　Moreover,　the　effects　of　vegetation　and　built-up　land　on　the　urban　climate　vary　across　locations　with　different　wind　patterns.