Under the background of global warming, extreme temperature events in China are frequent in recent years,which cause serious influence on economic development and daily life of people. For the evolution of monthly temperature is influenced by initial forcing and boundary forcing, its variation is complex, and brings great challenge to climate prediction. A quantitative investigation is carried out on the monthly temperature predictability limit based on the nonlinear local Lyapunov exponent and daily temperature from 1960 to 2011 at 518 stations in China. But to get robust nonlinear local Lyapunov exponent, there should be enough observations. How much can the length of data series affect the robustness of monthly temperature predictability limit? And what about the credibility of monthly temperature predictability limit? These two questions need to be further analyzed. Based on the nonlinear local Lyapunov exponent and nonlinear error growth dynamics,quantitative analysis is carried out, and it shows that the robustness of monthly temperature predictability limit depends on the length of data series,especially in Northwest China,Northeast China and Central China. In western Inner Mongolia, south of the Yangtze River and South China, data series need to be more than 30 years long. On average,45-year data series can ensure the stable monthly temperature predictability limit. The length of data series of 518 meteorological stations chosen in this study is 52 years,i. e.,they all fit the basic need to evaluate monthly temperature predictability limit. To verify the credibility of monthly temperature predictability limit, the spatial pattern of monthly temperature predictability limit and two objective monthly temperature prediction results are compared. One method is persistent prediction, and the other is monthly dynamic extended range forecast based on climate models. It shows that the spatial distribution of monthly temperature predictability limit and prediction skill is very consistent. The monthly temperature predictability limit evaluated by observation in January is lower than that in July. Similarly, the prediction skill in January is also lower than that in July. What's more, the spatial pattern of objective climate prediction skill in January (July) is similar to the spatial pattern of monthly temperature predictability limit in the respective month. Thus, the monthly temperature predictability limit estimated by nonlinear local Lyapunov exponent and daily temperature from 1960 to 2011 at 518 stations is scientific and credible. And it provides important reference for improvement of monthly temperature prediction.