Adverse　weather　has　a　considerable　impact　on　the　behavior　of　drivers,　which　puts　vehicles　and　drivers　in　hazardous　situations　that　can　easily　cause　traffic　accidents.　This　research　examines　how　drivers’　perceived　risk　changes　during　car　following　under　different　adverse　weather　conditions　by　using　driving　simulation　experiment.　An　expressway　road　scenario　was　built　in　a　driving　simulator.　Eleven　types　of　weather　conditions,　including　clear　sky,　four　levels　of　fog,　four　levels　of　rain　and　two　levels　of　snow,　were　designed.　Furthermore,　to　simulate　the　car-following　behavior,　three　car-following　situations　were　designed　according　to　the　motion　of　the　lead　car.　Seven　car-following　indicators　were　extracted　based　on　risk　homeostasis　theory.　Then,　the　entropy　weight　method　was　used　to　integrate　the　selected　indicators　into　an　index　to　represent　the　drivers’　perceived　risk.　Multiple　linear　regression　was　applied　to　measure　the　influence　of　adverse　weather　conditions　on　perceived　risk,　and　the　coefficients　were　considered　as　indicators.　The　results　demonstrate　that　both　the　weather　conditions　and　road　type　have　significant　effects　on　car-following　behavior.　Drivers’　perceived　risk　tends　to　increase　with　the　worsening　weather　conditions.　Under　conditions　of　extremely　poor　visibility,　such　as　heavy　dense　fog,　the　measured　drivers’　perceived　risk　is　low　due　to　the　difficulties　in　vehicle　operation　and　limited　visibility.　©　2019,　The　Author(s).