Using the actual snowstorm data (24-h snowfall p≥10.0 mn) at 122 national meteorological stations in Shandong from January to March and from November to December between 2017 and 2021, 4 methods are compared in the evaluation of snowstorm forecasting, including binary classification method, neighborhood spatial verification method, time migration method, and magnitude fuzzing method. The results are as follows. (1) Snowstorms in Shandong have obvious spatial and temporal distribution characteristics, and mainly appear in the north of Shandong Peninsula, while the probability of snowstorms occurring in the southeast of Shandong Province and the south of Shandong Peninsula is the lowest. The interannual and intermonthly variations of the occurrence of snowstorms are significant. The largest number of snowstorms that occur within one year is 98 and the smallest number is only 5; February is the peak month for the occurrence of snowstorms, accounting for 38.5% of the total number of snowstorms annually. (2) The probability of detection (POD) computed by the most widely used binary classification method at present is relatively low, with the POD of 24-h snowstorm forecasting only 12.08%. The main reason is that this method is affected by multiple factors, such as space, time, and magnitude, so it cannot accurately reflect the prediction ability. (3) The PODs of the neighborhood spatial verification method, time migration method, and magnitude fuzzing method for 24-h snowstorm forecasting are 14.40%, 14.69%, and 30.05%, respectively. Compared with the binary classification method, the PODs of these three methods significantly increase, while the false alarm rates and missing rates significantly decrease. (4) A comprehensive verification method that integrates the neighborhood spatial verification method, time migration method, and magnitude fuzzing method can distinguish the forecasting differences from three dimensions, namely space, time, and magnitude. The more precise and accurate verification results are conducive to guiding forecasters to put down the burden of low verification scores and make scientific and objective predictions, improving forecasting services.