Abstract: Abstract: Agricultural drought has been one of the most damaging natural hazards in the world, due mainly to the water shortage. A timely and effective monitoring system can greatly contribute to the management and mitigation of agricultural drought for better crops yields. A drought index can be further used to support the agricultural drought monitoring, assessment, and decision-making on mitigation measures. Therefore, it is a high demand to determine the real drought and monitoring index in practice. Taking the Shandong Province in eastern China as the research area, this study aims to construct a new agricultural drought monitoring model by random forest using the Crop Water Stress Index (CWSI). A deviation correction was also used to construct the Bias-corrected Random Forest Drought Condition Index (BRF-DCI). The physical meaning of evapotranspiration data was elucidated in the occurrence of agricultural drought. The multisource remote sensing was selected, including the Vegetation Condition Index (VCI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Standardized Precipitation Evapotranspiration Index (SPEI). The accuracy of the model was evaluated by the determination coefficient, and root mean square error. Since the study area presents the warm temperate continental monsoon climate with large temporal and spatial changes in the precipitation, some considerations were made on the influence of evapotranspiration on the drought monitoring model, as well as the accuracy and application of drought condition index for different drought grades. The results were as follows: 1) A better performance was achieved, when adding the CWSI as the independent variable into the drought monitoring model, where the determination coefficient of the BRF-DCI index and the observed SPEI-3 was 0.72-0.85, and the root mean square error was 0.58-0.71. 2) The BRF-DCI index with CWSI improved the accuracy of extreme drought monitoring, where the maximum monitoring accuracies of moderate, severe, and special drought were 0.88, 0.89, and 0.91, respectively. As such, the CWSI independent variable significantly improved the accuracy of the model, particularly for the extreme drought monitoring. 3) The drought monitoring index was basically consistent with the drought trend, represented by the real SPEI-3 at different sites, suitable for the changing of the actual drought. 4) The simulation of historical drought using drought condition index was also basically consistent with the actual in the study area. Consequently, the BRF-DCI can be widely expected to accurately predict the drought-affected areas with the temporal and spatial evolution. This finding can provide an important reference to evaluate the agricultural drought monitoring index for the early warning of natural hazards.