Context: Drip-irrigated maize is pivotal for food security in arid regions, yet its nitrogen (N) management remains inefficient. This inefficiency stems from a lack of tailored diagnostic tools. Specifically, critical nitrogen dilution curves (CNDCs), a core tool for precision N management, have not been adapted to the unique growth and environmental conditions of drip-irrigated high-density maize systems. Objective: To address the gap of unadapted CNDCs for drip-irrigated high-density maize, this study aimed to develop leaf dry matter (LDM)- and leaf area index (LAI)-based CNDCs for this system in northern Xinjiang, China, validate the performance of the developed CNDCs, and establish a non-destructive N diagnosis method. Methods: A Bayesian hierarchical model was used to develop the CNDCs. Data were collected from experiments involving 5 maize cultivars and multiple N rates. Six datasets were utilized to construct the LDM-CNDC and LAICNDC, while 2 independent datasets were used for validation. Additionally, normalized SPAD values were tested to evaluate their feasibility for non-destructive N diagnosis. Results: Two CNDCs were developed: LDM-CNDC (Nc = 3.47 & times; LDM- 0.43) and LAI-CNDC (Nc = 4.75 & times; LAI- 0.50), and the LDM-CNDC outperformed the LAI-CNDC in multiple aspects. It maintained significant correlations with soil organic matter (SOM), total N (TN), and rainfall, whereas LAI-CNDC parameters showed no meaningful environmental associations; for N nutrition index (NNI) estimation, the LAI-CNDC systematically underestimated N status (5.8-8.3% lower than LDM-based NNI), while LDM-based NNI values (0.98-1.02 under 320 kg & sdot;ha- 1 N) aligned with maximum growth thresholds; the LDM-CNDC also achieved higher relative yield (RY) prediction accuracy (R2 = 0.984-0.986) compared to the LAI-CNDC (R2 = 0.842-0.866); additionally, normalized SPAD values correlated strongly with LDM-based NNI (R2 = 0.7121), enabling rapid in-field non-destructive N diagnosis. Conclusion: The LDM-CNDC is more suitable for precision N management of drip-irrigated high-density maize than the LAI-CNDC. It better reflects environmental associations, estimates N status accurately, predicts RY reliably, and supports non-destructive N diagnosis, making it the preferred tool for this system. Implications: The LDM-CNDC improves N use efficiency (NUE), mitigates environmental risks from overfertilization, and sustains yields, providing a scalable solution for arid-region intensive maize systems where water and N efficiency are critical.