Abstract
South Asia, home to over a quarter of the global population, faces escalating climate risks that demand scientifically credible and actionable information. However, existing global climate models exhibit persistent temperature and precipitation biases—variables central to impact assessments—reaching up to 25% and 100% of their mean values, respectively, over this region, thereby limiting their reliability for climate-informed long-term planning. To address these limitations, we introduce the South Asia Regional Climate Information (SARCI) framework—a regionally optimized framework designed to deliver credible, high-fidelity climate information for South Asia. The framework features a customized atmospheric model, based on NCAR CESM/ CAM, with targeted improvements in deep convection, land–atmosphere interactions, and gravity wave dynamics—processes linked to major regional biases. These enhancements are guided by empirical understanding of regional climate behavior and refined through rigorous model tuning to achieve regional improvements without compromising global performance. The customized model substantially improves simulations of temperature and precipitation, along with a more realistic representation of regional circulation. The framework further incorporates a synthesized lower-boundary forcing component derived from skill-based CMIP models, adjusted to reduce biases in its low-frequency variability. A statistical downscaling module then refines the projections to a quarter-degree resolution, providing fine-scale, policy-relevant regional climate information. The SARCI framework demonstrates how regional optimization, coproduction, and institutional capacity building can deliver credible, policy-relevant climate information for South Asia, with broader relevance for other regions of the Global South facing similar challenges.
| Original language | English |
|---|---|
| Pages (from-to) | E564-E584 |
| Journal | Bulletin of the American Meteorological Society |
| Volume | 107 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2026 |
| Externally published | Yes |
Keywords
- Climate change
- Climate models
- Climate services
- Model evaluation/ performance
- Parameterization
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