Ground　surface　characteristics　(i.e.,　topography　and　landscape　patterns)　are　important　factors　in　geographic　dynamics.　Thus,　the　complexity　of　ground　surface　is　a　valuable　indicator　for　designing　multiscale　modeling　concerning　the　balance　between　computational　costs　and　the　accuracy　of　simulations　regarding　the　resolution　of　modeling.　This　study　proposes　the　concept　of　comprehensive　surface　complexity　(CSC)　to　quantity　the　degree　of　complexity　of　ground　by　integrating　the　topographic　complexity　indices　and　landscape　indices　representing　the　land　use　and　land　cover　(LULC)　complexity.　Focusing　on　the　meteorological　process　modeling,　this　paper　computes　the　CSC　by　constructing　a　multiple　regression　model　between　the　accuracy　of　meteorological　simulation　and　the　surface　complexity　of　topography　and　LULC.　Regarding　the　five　widely　studied　areas　of　China,　this　paper　shows　the　distribution　of　CSC　and　analyzes　the　window　size　effect.　The　comparison　among　the　study　areas　shows　that　the　CSC　is　highest　for　the　Chuanyu　region　and　lowest　for　the　Wuhan　region.　To　investigate　the　application　of　CSC　in　meteorological　modeling,　taking　the　Jingjinji　region　for　instance,　we　conducted　Weather　Research　and　Forecasting　Model　(WRF)　modeling　and　analyzed　the　relationship　between　CSC　and　the　mean　absolute　error　(MAE)　of　the　temperature　at　2　meters.　The　results　showed　that　the　MAE　is　higher　over　the　northern　and　southern　areas　and　lower　over　the　central　part　of　the　study　area,　which　is　generally　positively　related　to　the　value　of　CSC.　Thus,　it　is　feasible　to　conclude　that　CSC　is　helpful　to　indicate　meteorological　modeling　capacity　and　identify　those　areas　where　finer　scale　modeling　is　preferable.