Accurate long horizon forecasting of particulate matter (PM) concentration fields is essential for operational public health decisions. However, achieving reliable forecasts remains challenging in regions with complex terrain and strong atmospheric dynamics such as East Asia. While foundation models such as Aurora offer global generality, they often miss region-specific dynamics and rely on non-real-time inputs, limiting their practical utility for localized warning systems. To address this gap, we construct and release the real-world observations and high-resolution CMAQ-OBS dataset for East Asia, reducing regional error by 59.5% and enabling real-time 48-120 hour forecasts critical for public health alerts. However, standard point-wise objectives cannot reflect asymmetric operational costs, where false alarms deteriorate public trust while missed severe events endanger populations. This cost mismatch causes SFT models to over-predict and yield high False Alarm Rates. We introduce Group-Relative Policy Optimization (GRPO) with class-wise rewards and curriculum rollout to align predictions with operational priorities. Experimental results demonstrate that our framework significantly improves the reliability of the forecast. Compared to the SFT-only baseline, our model reduces the False Alarm Rate by 47.3% while achieving a competitive F1-score, proving its effectiveness for practical, real-world air quality forecasting systems on long lead time scenarios. Code and dataset are publicly available at https://github.com/kaist-cvml/FAKER-Air.

翻译：颗粒物（PM）浓度场的精确长期预报对于公共卫生决策至关重要。然而，在地形复杂、大气动力活动强烈的区域（如东亚地区），实现可靠预报仍具挑战性。尽管Aurora等基础模型具备全球普适性，但其常缺失区域特有动态特性，且依赖非实时输入，限制了其在本地化预警系统中的实际应用。为填补这一空白，我们构建并公开了东亚地区的真实观测数据与高分辨率CMAQ-OBS数据集，将区域误差降低59.5%，并实现了对公共卫生警报至关重要的48-120小时实时预报。然而，标准逐点优化目标无法反映非对称性运维成本——虚报会削弱公众信任，而漏报严重事件则危及人群安全。此类成本错配导致SFT模型过度预测，产生高虚警率。我们提出基于类奖励与课程策略演化的分组相对策略优化（GRPO），使预测结果与运维优先级对齐。实验结果表明，我们的框架显著提升了预报可靠性。与仅使用SFT的基线模型相比，本模型在保持竞争性F1分数的同时，将虚警率降低了47.3%，验证了其在长预报时效场景下实际空气质量预测系统中的有效性。代码与数据集已公开于https://github.com/kaist-cvml/FAKER-Air。