Recorded seismic observations from monitoring networks provide a direct empirical basis for updating regional representations of earthquake demand, yet their use in scenario forecasting and proactive resilience planning remains limited. This study proposes a monitoring-informed sense&ndash;forecast&ndash;adapt workflow for forecasting spatial seismic demand in the İzmir Basin, western T&uuml;rkiye. The framework transforms accumulated strong-motion records into a regional residual correction layer, embeds this layer within active-fault-based earthquake scenarios, and translates the resulting demand fields into adaptation-oriented spatial indicators.</p> <p>A transparent reference ground-motion representation is first fitted using event magnitude, source-to-site distance, and site-condition information. Event&ndash;station residuals are then aggregated at the station level and interpolated to construct monitoring-informed residual and residual-variability fields. These updating layers are embedded within five representative active-fault scenarios around the İzmir Basin to generate baseline and monitoring-updated PGA fields. The resulting demand fields are evaluated through persistence, scenario sensitivity, monitoring/inspection priority, rank variability, and spatial concentration of high-demand domains.</p> <p>The workflow is evaluated through residual-field validation, Gaussian-process benchmarking, robustness analyses, cumulative model-version tracking, and Monte Carlo uncertainty propagation. The results identify the Tuzla Fault scenario as the dominant demand case and the Yenifo&ccedil;a Fault scenario as the main secondary case. High-demand zones organize into spatially coherent regions, several locations remain repeatedly critical across multiple source scenarios, and the virtual regional demand representation stabilizes as the monitoring archive expands from 1977&ndash;2010 to 1977&ndash;2025.</p> <p>The main contribution is an updateable forecasting workflow that integrates monitoring-derived residual updating, scenario-based spatial demand simulation, uncertainty-aware exceedance mapping, and resilience-oriented decision indicators. By transforming accumulated strong-motion observations into an evolving regional demand representation, the proposed framework moves beyond pointwise ground-motion estimation and provides a digital-twin-oriented basis for proactive seismic resilience planning in monitored basin environments.