The　probabilistic　prediction　of　peak　ground　acceleration　(PGA)　using　the　Fourier　amplitude　spectral　(FAS)　model　has　many　advantages　in　regions　lacking　strong　ground-motion　records.　Currently,　the　implementation　of　this　approach　for　the　calculation　of　annual　exceedance　rate　of　PGA　relies　on　Monte　Carlo　simulations　(MCSs).　However,　adopting　MCS　requires　many　times　calculations　of　PGA　from　FAS,　and　each　time　of　calculation　includes　complicated　integrals,　the　computational　cost　is　too　high　to　be　acceptable　for　practical　applications.　Therefore,　this　study　proposes　an　efficient　method　for　the　probabilistic　prediction　of　PGA　using　the　FAS　model.　For　this　purpose,　a　probabilistic　analysis　method,　referred　to　as　the　moment　method,　was　introduced　to　improve　computational　efficiency.　The　probability　distribution　of　PGA　was　approximated　using　a　three-parameter　distribution　defined　according　to　the　first　three　moments.　The　first　three　moments　of　the　PGA　were　obtained　based　on　the　point-estimate　and　dimension-reduction　integration　method.　Numerical　examples　were　conducted　to　verify　the　proposed　method.　It　was　found　that　the　proposed　method　not　only　performed　much　more　efficiently　than　using　MCS　in　calculating　the　annual　exceedance　rate　of　PGA　to　obtain　the　hazard　curve　but　also　provides　nearly　the　same　accuracy　as　MCS.