Background: To make early prediction of immunoglobulin A nephropathy (IgAN) before renal biopsy, we developed and validated a new non-invasive nomogram for the early prediction of IgAN in primary glomerulonephritis (GN) in south China. Methods: A total of 431 patients were included in this study and additional 113 patients were included as the independent test cohorts to validate our results. A stepwise regression model was used for features selection. Multivariate logistic regression analysis with 5-fold cross validation was used to validate the result of the stepwise selection. Performance of the logistic regression model was assessed with respect to its calibration, discrimination, and clinical usefulness. Independent test was assessed. Results: We developed a model incorporating age of patients and four clinical chemistry signatures, including serum IgA, serum albumin (ALB), serum phosphorus (P) and 24-hour urinary protein (24hUpro) and presented with a nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) reached 0.89 (95% CI: 0.86–0.92) and 0.88 (95%: 0.85-0.92) in the training set and validation set, respectively. The model also had good performance in independent test cohorts (AUC of 0.82, 95% CI: 0.75–0.90). The DCA and calibration plot of the model also shows good performance. Conclusions: The logistic regression model presented in this study incorporates age of patients, IgA, ALB, P and 24hUpro and can be conveniently used to facilitate the individualized prediction of IgAN.