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It has been widely reported that anthropogenic influences are detectable with high confidence in global warming. However, whether human activities have an impact on snowfall changes is still unclear. Here we based on phase 6 of the Coupled Model Intercomparison Project (CMIP6) multi-forcing dataset and the regularized optimal fingerprint method, the detection and attribution of various grades of snowfall (including annual snowfall (>0.1 mm/day), light snowfall (<2.5 mm/day), intense snowfall (>5 mm/day), and their corresponding days) changes in Eurasia (20 ° -90 °N , 10 °W -180 °E) were carried out. Results show that anthropogenic activity forcing (ANT) and greenhouse gas forcing (GHG) well reproduced the spatial-temporal characteristics of snowfall indices. The ANT influence is robustly detected in the decreased trend of snowfall days (snowday), light snowfall, and light snowfall days (light_day) at the 90% confidence interval, clearly separated from the natural forcing. Moreover, the GHG signals are detectable for decreases in the three snowfall indices, which only could be distinguished from the natural and aerosol forcings for snowday and light snowfall. Thus, anthropogenic activities may considerably account for the decreases in snowday, light snowfall, and light_day across Eurasia, wherein the changes in the first two indices dominated by GHG emissions. However, human influence detections fail for intense snowfall, and it is hard to detect on regional scales, except for North Asia. Finally, by the end of this century (2081–2100), the observation-constrained projections based on the detection and attribution analysis under two SSPs (new Shared Socioeconomic Pathway) scenarios exhibit that the scaled snowday, light snowfall, and light_day are expected to decrease by approximately 13.9 days (28.3 days), 24.8% (48.7%), and 4.3 days (8.8 days) under the SSP2–4.5 (SSP5–8.5) scenario with reference to the current climate (1995–2014). Our study highlights the need to improve climate model performance in simulating extreme snowfall to clear whether and to what extent human influence impacts it. In the past 35 years, the decrease in snowfall days (snowday), light snowfall, and light snowfall days (light_day) can be attributed to human influence. However, CMIP6 simulations may underestimate these observed changes. We utilize the observationally constrained techniques based on detection results to constrain the projections for snowfall changes. Our results show that by the end of the 21st century, the scaled snowday, light snowfall, and light_day are expected to decrease by approximately 13.9 days (28.3 days), 24.8% (48.7%), and 4.3 days (8.8 days) under the SSP2–4.5 (SSP5–8.5) scenario with reference to the current climate. [Display omitted] • In the past decades, the observed snowfall decreased while extreme snowfall increased across most of Eurasia. • The decrease in snowfall days and light snowfall could be attributed to anthropogenic influence dominated by GHG emissions. • CMIP6 models may underestimate the observed changes in snowfall. • The observation-constrained projections show a larger decrease for snowday and light snowfall than the raw CMIP6 outputs. [ABSTRACT FROM AUTHOR]