Comparing Statistical Downscaling and Arithmetic Mean in Simulating CMIP6 Multi-Model Ensemble over Brunei

The climate is changing and its impacts on agriculture are a major concern worldwide. impact of precipitation will influence crop yield water management. Estimation such using inputs from the General Circulation Models (GCMs) for future years therefore assist managers policymakers. It important to evaluate GCMs local scale an study. As result, under Shared Socioeconomic Pathways (SSPs) scenarios, namely SSP245, SSP370, SSP585, simulations mean monthly daily across Brunei Darussalam in Phase 6 Coupled Model Intercomparison Project (CMIP6) were evaluated. performance two multi-model ensemble (MME) methods compared this study: basic Arithmetic Mean (AM) MME statistical downscaling (SD) utilizing multiple linear regression (MLR). All bias-corrected scaling (LS), their validated metrics as Root Square Error (RMSE) coefficient determination (R2). adjusted during validation period (2010–2019) shows improvement, especially SD model with R2 = 0.85, 0.86 0.84 SSP370 respectively. Although models produced unsatisfying results producing annual precipitation. Future analysis that there be much lower average trend comparison observed trend. On other hand, forecasted AM predicted same rainfall baseline far future. projected near reduced by at least 27% 11% models, In long term, less changes (17%). While estimated decrease 14%.