Abstract: In recent years, with the tourism booming and the increasing demands for flower-appreciation, the prediction of flowering date of ornamentals plants becomes more and more important. For a long time, phenological models are widely used in agriculture field, but rarely applied in predicting flowering time of ornamental plants.Based on phenological data of three ornamentals plants (Prunus discoidea, Magnolia denudata and Amygdalus davidiana) in Beijing Area, corresponding meteorological data during the period of 1981-2012 at Haidian and Miyun meteorological stations, three phenological models (SW Model, UniChill Model and Statistical Model) for simulating the first flowering date and the full flowering date of the above three plants are developed. In the experimental process, the least square fitting is introduced in computing parameters, including linear least square fitting in Statistical Model and nonlinear least square fitting in SW Model and UniChill Model. Moreover, the simulating annealing approach is used to obtain the analytic solutions for SW Model and UniChill Model. Results show that SW Model performs well in simulating the first flowering date and the full flowering date of Prunus discoidea, the full flowering date of Magnolia denudata, and the first flowering date and the full flowering date of Amygdalus davidiana. Besides, SW Model is the most applicable model with the root mean square error (RMSE) of external verification between 1.93-3.58 days. UniChill Model ranks the second with the RMSE of 2.49-3.89 days, and Statistical Model has the largest uncertainty with the RMSE of 2.37-4.24 days. As far as prediction accuracy is concerned, SW Model also ranks the first, and for more than 85% of years, the prediction error is within 3 days.Above all, SW Model is recommended for predicting the flowering dates of the ornamental plants in Beijing Area. But Statistical Model based on daily average temperature, considering the comprehensive effect of light and moisture and plant physiological processes, may perform better. With the increasing urban heat island effect in Beijing Area, the deviation caused by urban heat island effect should be removed during the application of SW Model.