Quality control (QC) of ground-based solar radiation measurements is fundamental to ensuring the integrity of surface energy balance and climatological studies. The extremely rare limit (ERL) test, a widely implemented QC standard, is frequently noted for being overly conservative, often failing to isolate subtle instrumental or environmental anomalies. To improve QC tightness and sensitivity, this study presents a data-driven framework for configuring regime-specific climatological limits. Diverging from traditional climate classifications that do not directly account for radiative variability, we define seven distinct radiation regimes through unsupervised learning, utilizing principal component analysis and hierarchical clustering. For each identified regime, optimal test coefficients are established via a machine-learning-based optimization strategy. Specifically, we maximize the <em>F</em>₁ score by benchmarking the climatological limit test against an isolation forest outlier detection model. Validation using global measurements from the Baseline Surface Radiation Network demonstrates that the proposed regional limits provide a significantly tighter fit to observed data distributions compared to the original global ERL thresholds. This methodology offers a scalable and automated approach to regionalizing QC procedures, substantially enhancing the precision of global radiation monitoring networks.