Abstract: To address the issues of excessive phosphorus (P) fertilizer application, low utilization efficiency, and the lack of a scientific P nutrition diagnosis system for drip-irrigated spring maize in northern Xinjiang, this study aimed to construct a universal critical phosphorus concentration dilution curve (CPDC) suitable for the region and verify its performance in precise P topdressing through field experiments. In 2023, multi-site field experiments were conducted in typical drip-irrigated maize areas of northern Xinjiang (Baiyang, Huyanghe, Shihezi). Different maize varieties and P application rate treatments were designed, and intensive sampling was performed to collect data on aboveground biomass and P concentration at various growth stages. A universal CPDC was successfully established using the Bayesian hierarchical model. The results demonstrated that the constructed universal CPDC could accurately quantify the dynamic dilution relationship between maize P concentration and aboveground biomass, with strong parameter stability and a concentrated 95% confidence interval. The 2024 verification experiment indicated that the model exhibited excellent prediction accuracy, with a root mean square error (RMSE) of 0.052 and a normalized root mean square error (n-RMSE) of 11.0% between the measured and predicted P concentrations. The slope of the scatter trend line was 1.02 with low dispersion, confirming the model's reliable estimation capability. Regarding the topdressing effects, the PR1.2 treatment performed optimally. Its maize yield was not significantly different from that of conventional fertilization, achieving a high-yield level, while the P fertilizer application rate was reduced by 13.1% compared with CF. Meanwhile, the partial factor productivity and utilization efficiency of P fertilizer in the PR1.2 treatment were increased by 13.7% and 22.1% respectively compared with CF. Additionally, this treatment effectively promoted the accumulation of aboveground dry matter in maize, with the biomass at maturity showing minimal difference from that of conventional fertilization. The universal CPDC constructed in this study innovatively quantifies parameter uncertainty through the Bayesian hierarchical model, overcoming the limitations of traditional methods. It is not dependent on specific variety or regional data and is adaptable to the common characteristics of drip irrigation cultivation patterns in northern Xinjiang. This model enables real-time diagnosis of P nutrition and precise topdressing throughout the entire growth period of maize, reducing the risk of P loss while ensuring food security. It provides technical support and theoretical reference for the green and efficient production of drip-irrigated spring maize and the optimized management of P fertilizer in northern Xinjiang, holding significant potential for widespread application.