Toward Value-Aware Metadata for Extreme-Event Retrieval in Cloud-Native Climate Data Services

Cloud-native climate services increasingly expose analysis-ready Zarr archives through web APIs and Python/Xarray workflows. These layouts accelerate coordinateto-value subsetting, yet they are less efficient for value-tocoordinate workloads, including threshold-based extreme-event discovery and regional top-$k$ retrieval. In such queries, geometryoverlapping chunks are often read and decompressed before value predicates can be evaluated, causing unnecessary data access. This paper presents an external value-aware metadata layer that preserves the original archive while enabling pruning before data access. The proposed sidecar maintains lightweight chunk synopses, coarse upper bounds, and hot metadata caches to transform geometry-only candidates into value-aware candidates. ERA5 experiments show up to $5 \times$ fewer chunk reads and up to $6.3 \times$ lower median latency than a geometry-only Zarr+Xarray pipeline, demonstrating a practical direction for low-latency extreme-event services without migrating data into a dedicated array database.