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<strong class="journal-contentHeaderColor">Abstract.</strong> Spotting, the process by which burning firebrands are lifted by convection and transported downwind igniting secondary fires. Spotting can become a critical driver of rapid wildfire spread and presents major challenges for prediction and suppression. Coupled fire–atmosphere models, which simulate the two-way interaction between fire behaviour and local atmospheric dynamics, offer a promising avenue to capture such complex processes. In this study, we introduce a computationally efficient Eulerian formulation for firebrand transport and spotting, implemented within the coupled MesoNH–ForeFire modelling framework. Two case studies were analysed: an idealized scenario over flat and hilly terrain to assess wind influence, and a realistic simulation of the 2016 Mt Bolton wildfire in southeastern Australia. The model captured key spotting dynamics and fire spread patterns, producing realistic downwind distances with spot fire timing that slightly preceded observations. A full 8-hour forecast, including spotting, simulates in just less than 3 hours, without optimization. Results demonstrate that this simplified approach provides a credible and time-efficient spotting forecast, supporting its potential for operational wildfire modelling and decision-making.