The maximum signal-to-noise empirical orthogonal function (MSN EOF) method is used to evaluate the midsummer 2-m air temperature (T2m) over Eastern China of subseasonal to seasonal scale forecast data in ECMWF model, and investigate the underlying mechanisms between temperature modes and predictable sources. The first predictable pattern mainly presents the dipole mode of positive value in the south and negative value in the north. The model captures the signal of the transition from preceding El Niño to La Niña and accompanying tropical Indian Ocean warm surface temperature. In the summer of transforming years, the West Pacific Subtropical High is stronger and westward, meanwhile the southwest monsoon strengthens, which are the main direct influence factors of the high pressure in the south and the more precipitation in the north. Compared with observations, although the model captures the relationship between the temperature mode and the previous sea surface temperature signal, it obscures the mediating role of the Western Pacific Subtropical High. The second predictable pattern is the warmer characteristic of the Yangtze River valley (YRV), and North Atlantic Oscillation which the atmospheric internal variability is the main signal. The wave train propagating from northwestern Russia to Northeast Asia is the main cause of the abnormal high pressure over YRV. The third mode is mainly the temperature trend item, and the spatial characteristics of observation and model are quite different. ECMWF model shows high forecasting skills in the three modes, and presents high (low) surface pressure in areas with high (lower) temperatures, reduced (increased) precipitation and increased (reduced) solar radiation, which proving the model simulates the potential mechanism of circulation anomalies affecting surface air temperature commendably.