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<p>Global climate models (GCMs) are fundamental tools which generate climate change projections that support decision making. However, their coarse spatial resolution limits their ability to capture local impacts accurately. To address this, dynamically downscaled projections are employed, aiming to refine global model outputs for finer-scale analyses. This study examines whether regional downscaling adds value to GCMs, focusing on rainfall extremes in the Maritime Continent (MC). We compare outputs from the downscaled regional climate model (RCM) simulations from the Singapore Variable Regional Climate Model (SINGV-RCM) with those from the Coupled Model Intercomparison Project Phase 6 ensemble (CMIP6). This study focuses on a new approach regarding regional downscaling’s added value (AV) which is usually used to assess the benefits of highest spatial resolution modelling. Here we emphasize the importance of comprehensively evaluating added value on broader-scale projections of climate extremes. Our investigation has highlighted significant areas where SINGV-RCM outperformed GCMs in the historical climate and investigated the value of these improvements in the context of climate projections. Although both sets of models indicated a similar direction of change in climate futures, SINGV-RCM exhibited a notably greater magnitude of change. In response to increasing anthropogenic warming, these changes are potentially further magnified in regions where SINGV-RCM added value. This difference in the magnitude of change emphasizes the importance of considering the unique contributions and limitations of both RCMs and GCMs when assessing the regional impacts of climate change, particularly in the context of extreme precipitation events. This study looks at whether regional downscaling improves the accuracy of rainfall predictions in the Maritime Continent. Researchers compared two types of climate models: the Singapore Variable Regional Climate Model (SINGV-RCM), a regional climate downscaling model, and the global climate models (GCMs) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The authors found that SINGV-RCM provided a more accurate representation of historical extreme rainfall compared to GCMs. The regional model also predicted bigger changes in rainfall due to climate change. This suggests that regional downscaling can offer valuable insights, especially for understanding extreme weather events, and highlights the need to consider both types of models when making climate projections.</p>

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Regional downscaling increases projected changes of maritime continent large scale rainfall extremes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

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  • Francois Delage,
  • Sugata Narsey,
  • Gerald Lim,
  • Xin Rong Chua,
  • Chen Chen,
  • Muhammad Eeqmal Hassim,
  • Sandeep Sahany,
  • Aurel Moise,
  • Venkatraman Prasanna,
  • Jianjun Yu,
  • Pavan Harika Raavi,
  • Fei Luo

摘要

Global climate models (GCMs) are fundamental tools which generate climate change projections that support decision making. However, their coarse spatial resolution limits their ability to capture local impacts accurately. To address this, dynamically downscaled projections are employed, aiming to refine global model outputs for finer-scale analyses. This study examines whether regional downscaling adds value to GCMs, focusing on rainfall extremes in the Maritime Continent (MC). We compare outputs from the downscaled regional climate model (RCM) simulations from the Singapore Variable Regional Climate Model (SINGV-RCM) with those from the Coupled Model Intercomparison Project Phase 6 ensemble (CMIP6). This study focuses on a new approach regarding regional downscaling’s added value (AV) which is usually used to assess the benefits of highest spatial resolution modelling. Here we emphasize the importance of comprehensively evaluating added value on broader-scale projections of climate extremes. Our investigation has highlighted significant areas where SINGV-RCM outperformed GCMs in the historical climate and investigated the value of these improvements in the context of climate projections. Although both sets of models indicated a similar direction of change in climate futures, SINGV-RCM exhibited a notably greater magnitude of change. In response to increasing anthropogenic warming, these changes are potentially further magnified in regions where SINGV-RCM added value. This difference in the magnitude of change emphasizes the importance of considering the unique contributions and limitations of both RCMs and GCMs when assessing the regional impacts of climate change, particularly in the context of extreme precipitation events. This study looks at whether regional downscaling improves the accuracy of rainfall predictions in the Maritime Continent. Researchers compared two types of climate models: the Singapore Variable Regional Climate Model (SINGV-RCM), a regional climate downscaling model, and the global climate models (GCMs) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The authors found that SINGV-RCM provided a more accurate representation of historical extreme rainfall compared to GCMs. The regional model also predicted bigger changes in rainfall due to climate change. This suggests that regional downscaling can offer valuable insights, especially for understanding extreme weather events, and highlights the need to consider both types of models when making climate projections.