Wheat is a crucial global staple crop essential for food security. However, continuous rainy weather during its growth, particularly at maturation, can cause ear germination and moldiness, severely impacting yield and quality. To accurately monitor and evaluate the germination and moldiness of wheat ears under continuous rainy weather stress during the maturity period, a case study was conducted on a continuous rainy weather process in the western part of the Huang-Huai region of China in late May 2023. The study tackled wheat ear germination and moldiness using meteorological and satellite remote sensing data, focusing on disaster risk elements. It developed meteorological hazard factors from weather stress mechanisms and characterized resilience using remote sensing parameters based on the state and environment of the wheat. Thirty modeling factors were selected for analysis. Spearman correlation and ReliefF methods were used for feature selection in binary and severity classification tasks, while Pearson correlation was employed for predicting the ear germination and moldiness index (EGMI).The screened factors were combined based on meteorological and remote sensing types as well as the screening methods used, forming QXS, YGS, QYS, and QYP factor strategies. Subsequently, five classification models, including Logistic regression (LGR), and five regression methods, including multiple linear regression (MLR), were applied for binary classification and severity grading of wheat ear germination and moldiness, as well as for predicting and simulating the EGMI. The effectiveness of these models in identifying and grading wheat ear germination and moldiness was then compared. The results showed that the factors constructed and screened using various methods from the perspective of the disaster-causing process of continuous rain and the three elements of disaster risk could achieve the identification and severity grading of germination and moldiness using different classifiers. The accuracy score (AC) ranged from 0.649 to 0.811 in the binary classification of wheat ear germination and moldiness identification, with Kappa coefficients between 0.245 and 0.600. For the three-category classification of severity grading, the AC ranged from 0.432 to 0.622, with Kappa values between 0.099 and 0.414. The R2 of EGMI prediction ranged from 0.10 to 0.25, with an average of mean absolute error (MAE) of 12.93 and an average of root mean square error (RMSE) of 16.74. The XGBR-QYP model performed the best, with R2, RMSE, and MAE values of 0.25, 15.69, and 12.05, respectively, as well as standard deviation (SDEV) and centered root-mean-square deviation (CRMSD) values of 13.10 and 15.55. Through comparative analysis of the three models, it was found that the remote sensing model was superior to the meteorological model in identifying germination and moldiness, while the meteorological model outperformed the remote sensing model in grading the severity of germination and moldiness. The integrated meteorological-remote sensing model, which combines the two, balanced their shortcomings and exhibited better performance and robustness. The models established in this study achieved the estimation of continuous rainy weather disasters in the western Huang-Huai region, filling the technological gap in monitoring wheat ear germination and moldiness, and providing technical support for wheat disaster reduction and post-disaster assessment.