For　a　sustainable　public　transport　system,　it　is　important　to　unveil　the　spatiotemporal　characteristics　of　ridership　and　identify　the　influence　mechanisms.　Some　studies　analysed　the　effects　of　weather　and　built　environment　separately,　however,　their　effects　when　incorporated　remains　to　be　determined.　Using　smart　card　data,　weather　information,　and　point　of　interest　data　from　Beijing,　the　Light　Gradient　Boosted　Machine　was　employed　to　investigate　the　relative　importance　of　weather　and　built　environment　variables　contributing　to　daily　ridership　at　the　traffic　analysis　zone　level,　and　investigate　the　non-linear　relationship　and　interaction　effects　between　them.　Weather　conditions　and　built　environment　contribute　30.22　and　55.83%　to　ridership　fluctuations,　respectively.　Most　variables　show　complex　non-linear　and　threshold　effects　on　ridership.　The　interaction　effects　of　weather　and　weekend/public　holiday　have　a　more　substantial　influence　on　ridership　than　weekdays,　indicating　weather　conditions　have　less　impact　on　regular　commuting　trips　than　discretionary　trips.　The　ridership　fluctuations　in　response　to　changing　weather　conditions　vary　with　spatial　locations.　Adverse　weather,　such　as　strong　wind,　high　humidity,　or　heavy　rainfall,　has　a　more　disruptive　impact　on　leisure-related　areas　than　on　residence　and　office　areas.　This　study　can　benefit　stakeholders　in　making　decisions　about　optimising　public　transport　networks　and　scheduling　service　frequency.