Abstract: The BCC/RCGWG is a stochastic weather generator developed for simulating daily precipitations in China. In this study it is further developed by adopting a framework based on the Fourier series with multivariables weak stationary process assumed for daily nonprecipitation variables including maximum temperature, minimum temperature, sunshine hour, relative humidity and average wind speed. These nonprecipitation variables are made dependent on the simulated precipitation by the previous version of the BCC/RCGWG for daily precipitation. Using daily observations of the five nonprecipitation variables from 1971 to 2000, the parameters for each nonprecipitation variable of the further developed weather generator are determined for the 669 stations all over China. Based on the estimated parameters, daily nonprecipitation variables at these stations for any period of time can be simulated. A 100 year simulation is made and compared with observations during 1971-2000 in terms of annual, monthly statistics and extreme climate events. The results of monthly and annual statistics are fairly satisfactory. Root mean square error (RMSE) of annual maximum temperature, minimum temperature, sunshine hour, relative humidity and average wind speed are 0.2℃, 0.2℃, 70.9 h, 0.6% and 0.2 m/s, respectively. The RMSEs of monthly maximum temperature, minimum temperature, sunshine hour, relative humidity and average wind speed are between 0.4-0.7℃, 0.4-0.7℃, 10-20 h, 4%-14% and 0.6-0.9 m/s, respectively. These demonstrate the usefulness of the weather generator BCC/RCGWG for the five nonprecipitation variables. However, simulations of extreme climate events are less satisfactory.