Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

中文翻译：

---

预测次季节到季节性 (S2S) 时间尺度的全球火灾排放


火灾排放的气体和气溶胶会改变大气成分，并对气候、生态系统功能和人类健康产生重大影响。气候变暖和人类在易燃地区的扩张加剧了火灾影响，需要更有效的管理工具。在这里，我们开发了一个全球火灾预测系统，该系统使用过去的火灾数据和气候变量来预测 1 至 6 个月的交付周期内的每月排放量。我们使用全球火灾排放数据库 (GFED) 的每月火灾排放量作为预测目标，在 1,300 多个不同的火灾区域拟合统计时间序列模型，即具有外生变量的自回归综合移动平均模型 (ARIMAX)。然后使用优化的参数来预测未来的排放量。预报系统考虑了有关区域特定季节性、长期趋势、近期火灾观测以及代表大规模气候变化和当地火灾天气的气候驱动因素的信息。我们使用不同的预测变量和预测提前期组合交叉验证了系统的预测技能。该参考模型将内源和外源预测因素与 1 个月的预测提前期相结合，解释了全球火灾排放异常的 52% 的变异性，大大超过了假设预测期内持续排放的参考模型的性能。该系统还成功解决了火灾活跃地区火灾排放异常的详细空间模式。这项研究弥合了近实时火灾预报和季节性火灾展望之间的差距，代表着朝着建立可操作的全球火灾、烟雾和碳循环预报系统迈出了一步。