High-frequency　macro-financial　environment　variables　provide　more　useful　information　and　are　efficient　in　predicting　the　low-frequency　GDP　growth　rate.　To　this　end,　we　extend　the　traditional　Growth-at-Risk　(GaR)　into　a　high-frequency　GaR　(HF-GaR).　In　this　extension,　we　construct　three　high-frequency　macro-financial　environment　indices　using　a　mixed　frequency　dynamic　factor　model　and　then　use　a　mixed　data　sampling-quantile　regression　method　to　measure　China’s　daily　GaR　from　Jan　1,　2000,　to　Sep　30,　2024.　The　evidence　shows　that　our　HF-GaR　has　favorable　prediction　performance,　with　quantile　mean　absolute　error　and　quantile　root　square　error　values　less　than　0.1　and　is　significantly　superior　to　the　traditional　GaR　at　the　1%　level　for　most　quantiles.　Additionally,　HF-GaR　can　offer　early　warning　of　economic　downturns,　especially　predicting　China’s　GDP　growth　rate　at　the　5%　quantile　less　than　0　in　2020Q1.　Moreover,　we　conduct　a　counterfactual　scenario　analysis　and　find　that　the　conditional　quantile　of　GDP　growth　rate　changes　as　the　macro-financial　environment　tightens　or　loosens.　Finally,　we　also　validated　that　the　HF-GaR　model　is　equally　applicable　in　other　economies.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2025.