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Assimilation of thinned radar radial wind data can help improve the model's forecasting capability for short-term precipitation.However， the thinning method affects the distribution characteristics of the radar radial wind super-observations（SOs）， which in turn affects the assimilation and prediction results.This study investigates the impact of radar radial wind thinning method on rainfall forecasting through sensitive experiments.Based on a heavy rain event in North China， two experimental groups were conducted， employing radar radial wind SOs with varying grid resolutions （by altering radial spacing or azimuthal interval）.The results show that changing the radial spacing or azimuthal interval of super-observation box alters the extremes of the SOs and their locations， thereby influencing both the intensity and position of the jet stream.The radial spacing additionally affects the jet height by influencing the altitude at which the extremes of the SOs occur， and it has a relatively significant impact on the quantity of data obtained and the analysis error.The assimilation of radial wind SOs with different grid resolution similarly adjusts the wind field， which can increase the cyclone of the shear line in central and southern Hebei and the southerly component of the low-level jet in central Shandong.The grid resolution of the SOs mainly affects the curvature of the cyclonic shear and the intensity of the southerly jet.For precipitation forecasting， assimilation radar radial wind SOs can improve the overall performance of precipitation forecasts for the first 6 h of the model， particularly in capturing heavy precipitation events exceeding 25 mm.Meanwhile， it provides better scores for 24 h forecasts of light and moderate rainfall and can restrain some false precipitation forecasts.When a higher resolution of radial wind SOs is adopted， the forecasting performance for heavy precipitation within 12 h improves significantly.The threat score， false alarm ratio， and probability of detection （POD） of precipitation are more sensitive to changes in radial spacing during thinning， while variations in azimuthal interval have a relatively more pronounced impact on forecasting bias.