Abstract: Crop growth models can predict crop growth and development at different scales from the station to the regional level, requiring daily weather data to drive crop growth. The Weather Stochastic Simulator (SWG) can meet this demand. The research team constructed a random weather generator based on the characteristics of China's climate (Weather Generator based on Dry and Wet Spells, WGDWS), and replicated the widely used WGEN series (Weather Generator) weather generator (Daily Weather Stochastic Simulator, Daily Weather Stochastic Simulator DWSS). In order to evaluate the applicability of meteorological data generated by WGDWS and DWSS as inputs for wheat growth models, this paper uses 57a of daily meteorological data measured at 8 stations in the northern winter wheat region of China, and generates 300a of daily meteorological data through WGDWS and DWSS, respectively. The quality of meteorological element statistical values generated by the two types of generators is analyzed. Except for the Taiyuan station in Shanxi, the monthly average highest and lowest temperatures generated by the two types of generators have achieved very good consistency with the measured values. The monthly average of total solar radiation generated by the two types of generators has good consistency and both have certain deviations from the measured values. Both overestimation and underestimation of monthly average rainfall exist. However, there was no significant difference between the monthly mean values of meteorological elements generated by the two types of generators and the measured values. A crop model Wheat Intelligent Decision-making System was used to evaluate the impact of measured and generated data on wheat growth simulation results under two management methods (potential production and experience water and fertilizer). The results showed that the mean wheat yield and biomass simulated by WGDWS and DWSS were slightly overestimated, and the simulation effect was good, with determination coefficients of 0.94 and 0.99 compared to the measured values, respectively. In terms of standard deviation for simulated yield and biomass, WGDWS has 13% and 44% lower than the measured weather by more than 20%. For DWSS, these values are 6% and 25%, respectively. In cases where the standard deviation is more than 10% lower than the actual measurement, WGDWS accounts for 50% and 63% respectively, while DWSS accounts for 31% and 56% respectively. Overall, the simulation effect of the two types of generators on biomass is better than that on yield, and the interannual variation of simulated biomass is also smaller than that of simulated yield. The simulation trends of WGDWS and DWSS for accumulated temperature, evapotranspiration, and dry matter accumulation are highly consistent. The simulation differences for accumulated evapotranspiration are slightly greater than those for accumulated temperature and dry matter, and they do not show significant differences from the measured values. There is no significant difference in the simulation results of yield and biomass mean between the two types of generators generating weather and measured weather. Therefore, the weather data generated by the two types of generators can be used instead of measured weather for wheat growth simulation. The mean relative error (MRE) and root mean square error (RMSE) of physiological indicators simulated by WGDWS are smaller than those of DWSS, except for dry matter weight. Compared with DWSS, the differences in yield and biomass simulated by WGDWS almost do not exceed 10%, with errors within 10% accounting for 94% and 100%, respectively. WGDWS has a 6% and 25% higher simulated yield and biomass with an error of less than 5%. The simulation results of yield, biomass, phenological period and other indicators using WGDWS and DWSS under two management methods were further analyzed. Results showed that among the 88 sets of data, the absolute error and standard deviation of WGDWS were 63 sets and 57 sets, respectively, with WGDWS superior to DWSS. Under the two management methods, there are 14 indicators with significant differences between the two types of generators. Among them, 13 indicators WGDWS is significantly better than DWSS, indicating that WGDWS has better simulation effect than DWSS. Therefore, the meteorological data generated by WGDWS can be fully used as input for crop growth models, and the simulation error for indicators such as yield and biomass is better than that of DWSS.