Irrigation forecasting is essential for improving water management in agriculture. This study proposed a novel and practical framework for irrigation forecasting for paddy rice. Public weather forecasts in China were selected to forecast ETo and quantitative rainfall. For the latter, a simple deterministic model and a probabilistic model based on a Pearson-III representation of the data were considered. A modified Python version of the AquaCrop model (ACOP-Rice model) was adopted to generate the probability distribution of forecasted irrigation events and rule-based irrigation recommendations. The performance of weekly irrigation forecasting was then evaluated. The analysis of the weather forecasts revealed that the accuracy of the temperature and ETo predictions was acceptable, whereas the quality of the rainfall forecasts was poor. The performance of the scenarios that using probabilistic rainfall forecasts outperformed scenarios that relied on deterministic rainfall forecasts, despite the lower quality of the probabilistic rainfall forecasts compared to the deterministic forecasts. The irrigation recommendations exhibited a systematic bias (i.e., the inclination toward overirrigation or underirrigation) when using weather forecasts, and this bias was lower when using probabilistic forecasts. Additionally, compared to ETo forecasts, the inaccuracy of rainfall forecasts had a much greater impact on irrigation forecasts.