<p>This study introduces a multi-metric diagnostic framework for district-level climate risk advisory in maize cultivation, using Telangana, India, as a case study. The framework combines correlation analysis, SHAP-based attribution, temporal validation, and operational diagnostics to identify key agro-climatic drivers and stratify forecast reliability. Across three seasonal windows, we found that minimum temperature during vegetative growth and early-season drought variability consistently emerged as dominant yield influencers. Districts such as Mancherial and Jangaon showed reliable forecast performance, while high-risk zones including Nirmal and Jayashankar exhibited unstable behavior and multiple critical warning events. In contrast, stable districts such as Jangaon and Kamareddy demonstrated consistent forecast reliability suitable for automated advisory integration. These findings highlight clear district-level contrasts between stable and high-risk zones and provide actionable insights for climate-smart agricultural planning. The proposed framework advances beyond conventional accuracy metrics, offering a reproducible tool for stratified model calibration, ensemble tuning, and adaptive district-specific planning under variable climatic regimes.</p>