The Yangtze River Basin(YRB) is one of the areas with a high frequency of heatwave occurrences in China.The daily maximum temperature (T_max) in this area shows significant low-frequency oscillation signals for (10—30 d and 30—60 d) time periods.Based on the results of the lead-lag correlation analysis between the YRB T_max and the 10—30 d/30—60 d convection and circulation anomalies,we identify the main low-frequency signals affecting the YRB T_max.There are three types of signals that travel in different directions:1) the eastward and southward signals from the Eurasian continent;2) circulation anomalies propagating southwestward from Northeast Asia;and 3) low-frequency convective signals propagating from the western Pacific toward East Asia.The temperature diagnostic equation results show that when the low-frequency convection/circulation anomalies approach the YRB,both the diabatic (clear-sky radiative heating) and adiabatic (associated with sinking motion) heating processes lead to variations in the YRB temperature.To identify these precursory signals objectively and efficiently,as well as consider the nonlinear interaction between YRB T_max and the large-scale predictors,we use Convolutional Neural Network (CNN),a type of deep neural network,to train the historical data,and then develop an extended-range forecast model for YRB T_max.The independent forecast results show that the CNN-based forecast model is capable of predicting the YRB T_max at a 30-day lead time,with the temporal correlation coefficient between the forecast and observed T_max of 0.63—0.70 (exceeding the 99％ confidence level).The current results suggest the potential of CNN in the application of extended-range forecasting as the magnitude of error (root-mean-square error) is less than one standard deviation.