Abstract: Based on the daily temperature and precipitation data from 13 meteorological stations in Harbin from 1961 to 2023,hazard index models were constructed for weather and climate events such as snow disasters,cold air,and heavy precipitation events,and a dataset was established.On this foundation,the paper analyzed the weekly price-fluctuation characteristics of various agricultural products in Harbin from 2021 to 2023,including grains,coarse grains,meat,eggs,poultry,aquatic products,vegetables,and fruits.This paper also delved into the correlation between the price fluctuations of various agricultural products and the hazard indices of weather and climate events over the preceding 7-day,14-day,and 21-day periods.Furthermore,machine-learning techniques,namely random forest,support vector machine,and BP neural network,were employed to evaluate the performance of agricultural product price-fluctuation models,both with and without the weather and climate event hazard index factor.The results show that the hazard index for snow-disaster events is formulated by taking into account the total volume of heavysnowfall during the heavy-snowfall event,the maximum daily snowfall during the heavy-snowfall period,and the average daily snowfall during the same period.For cold-air events,the hazard index is determined based on the duration of the cold-air event,the maximum temperature decline during the event,and the lowest temperature during the event.For heavy-precipitation events,the hazard index is denoted by the total precipitation amount during the heavy-precipitationevent.The price-fluctuation series for meat,eggs,poultry,vegetables,and fruits fail to display the characteristics of stationarity,autocorrelation,and heteroscedasticity.Models based on the auto-correlation of time series,which are commonly employed in predicting agricultural product prices,are inappropriate for the research subjects of this study.The correlation coefficients between the price fluctuations of agricultural products and the hazard indices of weather and climate events are not significant at the 0.05 level.Consequently,it is not practicable to solely utilize the hazard indices of weather and climate events to develop traditional statistical regression models for fitting and forecasting price fluctuations.Nevertheless,the correlation analysis reveals differences in the sensitivity of price fluctuations of various agricultural product categories to weather and climate events.Specifically,the price fluctuations of meat,eggs,and poultry are relatively strongly correlated with the weather and climate event indices of the preceding 7 days; those of fruits exhibit the highest correlation with the indices of the preceding 14 days; and the price fluctuations of vegetables show a strong correlation with the indices of the preceding 21 days.After introducing the factor of the hazard index of weather and climate events,both the mean squared error (MSE) and mean absolute error (MAE) of the random forest model for predictingthe price fluctuations of these three types of agricultural products are significantly reduced.In particular,for the prediction of the price fluctuations of meat,eggs,and poultry,the MSE and MAE decrease by 39.7% and 21.6%,respectively.