Calibration　of　agent-based　models　(ABM)　is　an　essential　stage　when　they　are　applied　to　reproduce　the　actual　behaviors　of　distributed　systems.　Unlike　traditional　methods　that　suffer　from　the　repeated　trial　and　error　and　slow　convergence　of　iteration,　this　article　proposes　a　new　ABM　calibration　approach　by　establishing　a　link　between　agent　microbehavioral　parameters　and　systemic　macro-observations.　With　the　assumption　that　the　agent　behavior　can　be　formulated　as　a　high-order　Markovian　process,　the　new　approach　starts　with　a　search　for　an　optimal　transfer　probability　through　a　macrostate　transfer　equation.　Then,　each　agent＇s　microparameter　values　are　computed　using　mean-field　approximation,　where　his　complex　dependencies　with　others　are　approximated　by　an　expected　aggregate　state.　To　compress　the　agent　state　space,　principal　component　analysis　is　also　introduced　to　avoid　high　dimensions　of　the　macrostate　transfer　equation.　The　proposed　method　is　validated　in　two　scenarios:　1)　population　evolution　and　2)　urban　travel　demand　analysis.　Experimental　results　demonstrate　that　compared　with　the　machine-learning　surrogate　and　evolutionary　optimization,　our　method　can　achieve　higher　accuracies　with　much　lower　computational　complexities.