Based on the GRAPESGlobal Ensemble Forecast System (GRAPESGEPS), and the nationwide coldwave process from 13 to 16 February 2020, the Kmeans cluster products are developed. In this paper, the Sum of the Squared Errors (SSE) criterion function is applied to determine the most appropriate clustering numbers and the Kmeans cluster algorithm is used to classify the ensemble samples. Results indicate that, all types of Kmeans cluster products related to the 500 hPa geopotential height present the Ωshaped circulation situation and the cold advection situation behind the lowpressure system. In addition, Type 1 clustering products with the highest probability reflect the observed circulation situation most efficiently. For 850 hPa temperature, all categories can present the spatial characteristics of 850 hPa temperature, which increase gradually from North China to South China. In addition, Type 1 clustering products with the highest probability can reflect the spatial distribution of 850 hPa temperature and possess the least errors related to the observation. For 10 m wind speed clustering products, at higher wind speeds, the dispersion of the aggregate samples is larger, and the wind speeds of different kinds have significant differences. The Type 1 clustering products with the highest probability can reflect the spatial distribution and intensity of 10 m wind speed in Tianjin and its surrounding areas exactly and provide valuable prediction information for forecasters. With Kmeans cluster results, we can realize the aggregation of forecast sample information and provide the intuitive guidance of weather prediction for the forecasters.