<![CDATA[Communicating long-term climate trends to non-specialist audiences remains a persistent challenge, despite the availability of well-validated global temperature datasets. While existing climate visualizations and reports provide accurate information, they often rely on expert-driven interpretations or static representations that limit accessibility and scalability. This study presents a proof-of-concept system that integrates analytical processing, rule-based narrative generation, and infographic synthesis to transform structured climate data into coherent public-facing communication artifacts. The proposed framework uses the NASA GISTEMP v4 dataset, covering annual global temperature anomalies from 1880 to 2024. It applies linear trend estimation and deterministic anomaly highlighting to extract salient temporal patterns. These analytical outputs are then translated into traceable natural language summaries and integrated with visual encodings within a single reproducible pipeline. The results confirm a persistent long-term warming trend, with several recent years exceeding high-anomaly thresholds, and demonstrate that analytical values, narrative descriptions, and visual emphasis can be generated consistently from a shared data source. Rather than introducing new climate indicators or predictive models, this study’s contribution lies in system-level integration: coupling data analysis, narrative synthesis, and visual composition into a unified, communication-oriented workflow. The framework is explicitly positioned as a proof of concept and does not claim causal attribution or empirical validation of user impact. Nonetheless, it demonstrates how transparent automation can reduce reliance on expert mediation while preserving scientific fidelity, supporting scalable climate communication systems.]]>
