Convolutional Neural Network (CNN) has been widely used in precipitation downscaling. However, it is unclear whether the predictor region size significantly influences the precipitation downscaling results using CNN for predictor-predictand mapping. In this paper, we perform sensitivity experiments on various predictor areas in CNN-based precipitation downscaling. Specifically, we select the middle reaches of the Yellow River (MRYR) as the study area (predictand region). For the predictor areas, we expand , , , and in four directions based on the MRYR, respectively. These sensitivity experiments indicate that the predictor region size significantly affects the precipitation downscaling results. The result of the precipitation downscaling expanded to over the MRYR performs best in Root Mean Square Error (RMSE) of spatial-temporal distribution. Specifically, on mean precipitation, it reduces RMSE by 5.71% and 12.77% relative to the Base experiment (predictor area is the MRYR) in space and time, respectively. For extreme precipitation, RMSE decreases by 2.12% (4.27%) and 12.9% (14.02%) in space and time compared to the Base experiment for R95P (R99P), respectively. Then, the downscaled precipitation results deteriorate when continuing to expand the predictor area. That is mainly because the thermodynamic and dynamic variables near the study area significantly affect local precipitation. When the predictor area is continuously expanded without restriction, the complexity of nonlinear relationships amongst climate variables may markedly increase, resulting in many redundant features during downscaling, thereby reducing downscaling performance. Therefore, our results suggest that appropriately expanding the predictor aera may positively influence the downscaling of regional precipitation.