Abstract: Under the background of climate change, extreme meteorological events have become increasingly frequent, yet the impacts of early spring low temperatures on subtropical artificial coniferous forests have received limited attention. Based on meteorological and gross primary productivity (GPP) data from the Qianyanzhou station during 2003–2019, this study selected days with daily mean temperatures below 5?°C (≥32 days) and cumulative temperatures ≤750?°C during January–March as early spring low-temperature hazard indicators. Principal component analysis was applied to quantify the relative contributions of these two low-temperature factors, and a comprehensive early spring low-temperature risk model was constructed to assess the impact of early spring low temperatures on GPP. The results indicate that early spring low temperatures are frequent at Qianyanzhou, with an occurrence probability of up to 23.2%. A comprehensive low-temperature index below 0.38 can be used to identify early spring low-temperature risk in artificial coniferous forests, effectively distinguishing low-temperature years. The assessment model of early spring low-temperature effects on artificial forest GPP shows that cumulative GPP in early spring is closely related to the comprehensive low-temperature index (R2=0.64, P<0.01), and annual GPP is significantly correlated with the index (R2=0.43, P<0.01). For every 0.1-unit decrease in the comprehensive early spring low-temperature index, cumulative GPP in early spring and annual GPP decrease on average by 12.5 g C·m?2 and 18.3 g C·m?2, respectively. These findings provide a scientific basis for quantitatively evaluating early spring low-temperature damage in artificial forest ecosystems.