Abstract: A mesoscale ensemble forecasting system based on the WRF-ARW (Advanced Research Weather Research and Forecasting) model was developed and an AgI cold-cloud seeding module was directly coupled to evaluate the precipitation-enhancement potential of stratiform clouds and quantify associated uncertainties in northern China. A typical snowfall event over North China from 12 February to 13 February 2022, is selected for investigation. This study employs a multimicrophysics ensemble and a multi-initial-condition ensemble to assess the seeding suitability of the target area. Moreover, an AgI-coupled ensemble is utilized to evaluate the seeding effects. The results indicate that the target cloud system possesses high seeding potential, with ensemble probabilistic forecasts showing a supercooled water content (＞0.2 g/kg) probability exceeding 85% and an ice crystal concentration (＜20 L^－1) probability surpassing 75%. Seeding experiments demonstrate that AgI particles enhance ice crystal formation and accelerate ice–water conversion, leading to increased precipitation. The precipitation enhancement ranges from 2.76% to 101.09%, with an ensemble mean of about 36.87%. This study innovatively applies nonparametric kernel density estimation to quantify uncertainty in seeding effects. The results reveal that the seeding process considerably alters the precipitation probability distribution. After seeding, the probability of precipitation enhancement exceeding 0.01 mm covers more than 90% of the target area, whereas that of precipitation enhancement exceeding 0.1 mm surpasses 70% of the target area. This study demonstrates that ensemble probabilistic forecasting can effectively quantify uncertainties in artificial cloud seeding, providing scientific support for seeding-condition prediction and seeding-effect estimation, thereby helping improve the scientific nature and precision of weather-modification operations.