Current water consumptions are unsustainable in many regions, which requiring more efficient agricultural water management strategies. This study incorporated the DSSAT-CERES-Maize model with a new algorithm for dynamic within-season irrigation scheduling for maize (Zea mays L.) based on trends in daily forecasted yields. Field experiments were undertaken at four arid and semiarid sites in Northwest China, including Changwu (2010 and 2011, rainfed), Yangling (2014 and 2015, irrigated), Jingyang (2015, irrigated), and Shiyanghe (2015, irrigated). Historical 50-year (1968–2017) weather data were available for each site. In daily yield forecasts, weather data before forecast dates were observed from local weather stations, while the unknown data between forecast and harvest dates were supplemented by local 50-year continuous weather series in the same periods. Then 50 maize yields could be obtained on each forecast day, and the median values were calculated as the prediction on that day. As the growing season advanced, historical weather data were gradually replaced by actual weather data. Further, the dynamics of daily forecasted yields were used to schedule irrigation based on a new algorithm. The new algorithm schedule irrigations by considering the feedbacks of maize grain yield to interactions of actual weather, environment, and management. The results showed that forecasted maize yield had considerable uncertainty before tasseling but rapidly converged to the actual yield about one month before harvest. The mean absolute relative errors (MAREs) of daily forecasted yields were 11.7% and 7.3% at Changwu in 2010 and 2011, respectively. Simulated irrigation use efficiency (IUE) for almost all sites and years were improved. The new irrigation scheduling algorithm will help to improve irrigation scheduling in arid and semiarid areas where precipitation is the main limited factor to maize yield.