In　this　article,　we　study　the　(group)　smoothly　clipped　absolute　deviation　(SCAD)　estimator　in　the　estimation　of　generalised　additive　models.　The　SCAD　penalty,　proposed　by　Fan　and　Li　[(2001)　＇Variable　Selection　via　Nonconcave　Penalised　Likelihood　and　Its　Oracle　Properties＇,　Journal　of　the　American　Statistical　Association　96(456),　1348-1360],　has　many　desirable　properties　including　continuity,　sparsity　and　unbiasedness.　For　high-dimensional　parametric　models,　it　has　only　recently　been　shown　that　the　SCAD　estimator　can　deal　with　problems　with　dimensions　much　larger　than　the　sample　size.　Here,　we　show　that　the　SCAD　estimator　can　be　successfully　applied　to　generalised　additive　models　with　non-polynomial　dimensionality　and　our　study　represents　the　first　such　result　for　the　SCAD　estimator　in　nonparametric　problems,　as　far　as　we　know.　In　particular,　under　suitable　assumptions,　we　theoretically　show　that　the　dimension　of　the　problem　can　be　close　to　exp{n(d/(2d+1))},　where　n　is　the　sample　size　and　d　is　the　smoothness　parameter　of　the　component　functions.　Some　Monte　Carlo　studies　and　a　real　data　application　are　also　presented.