Researchers have developed a practical pipeline that transforms complex climate data from NetCDF format into city-level risk insights. The approach bridges the gap between raw climate data processing and applied impact modeling, addressing the challenge of making petabyte-scale datasets useful for urban planning and decision-making.

The pipeline integrates CMIP6 projections and ERA5 reanalysis data with impact models, implementing a workflow from data ingestion through anomaly detection. It uses percentile-based thresholds to define extreme conditions locally rather than relying on global standards, and incorporates wet-bulb temperature calculations to better assess human heat stress.

A case study contrasting Jacobabad and Yakutsk demonstrates how the same pipeline yields different results based on local climate baselines. The method translates temperature anomalies into human impact metrics like estimated mortality and economic losses, providing actionable insights despite simplifying assumptions about population vulnerability.

This approach offers cities a structured methodology for climate risk assessment without requiring specialized climatology expertise. By converting multidimensional climate data into interpretable outputs, the pipeline enables more informed adaptation planning while acknowledging its limitations in capturing social and infrastructural variations in vulnerability.