The arXiv preprint (not peer-reviewed) introduces MeteoLogist, a radar-based framework that encodes thermodynamic, kinematic, and microphysical drivers to detect convection precursors before reflectivity spikes. Evaluated on 3D-NEXRAD volumes spanning 2020-2022 across the contiguous US, the model lifts critical success index at 40 dBZ (CSI40) by 9.7% overall and 37.67% in the developing stage versus strong deep-learning baselines. This gain arises from causal temporal attention aligning asynchronous signals and cross-regional aggregation that stitches fragmented precursors. Yet the study supplies no independent test set outside US radar coverage, omits extreme-event stratification, and leaves code unreleased beyond supplementary claims. Comparable earlier work, such as the 2023 Nature Machine Intelligence paper on MetNet-3 (Google Research, 2020-2022 US data), achieved smaller lead-time gains without explicit physics streams; a 2024 BAMS review of operational nowcasting further notes that precursor models often degrade when transferred to European or tropical regimes. Deployment within months therefore hinges on NOAA or private vendors completing real-time integration tests absent from the current manuscript.