Bridging the Climate Prediction Gap From Probabilistic Warnings Toward Deterministic Cascading Disaster Prevention

Climate change is accelerating across the Indian subcontinent. India Meteorological Depart- ment (IMD) data confirm a statistically significant national warming trend of approximately +0.61◦C to +0.68◦C per 100 years (1901–2025, depending on the reporting period), along- side increasing volatility in seasonal rainfall. Despite this well-documented trend, IMD’s publicly accessible bulletins provide only national- and subdivision-level summaries; gran- ular, station-level, multi-variable (humidity, wind, instability index) time-series data is not available through open bulletins and instead requires a formal data requisition to IMD, with no guarantee of fulfillment. This case study presents two distinct components, clearly sepa- rated by evidentiary status: (1) a verified analysis of IMD’s own published warming trend and its documented spatial/temporal monitoring limitations, and (2) an illustrative com- putational scenario – explicitly not derived from real sensor data – that demonstrates how a hypothetical dense sensor network combined with a coherence-scoring framework (CL5D) might process high-frequency micro-climate data, presented strictly as a concept requiring future empirical validation. We argue that the very difficulty of obtaining granular IMD data through normal public channels is itself evidence for the case study’s central thesis: India’s climate monitoring infrastructure has a real, practical accessibility gap that comple- mentary low-cost sensor networks could address – a claim that stands on its own without requiring the illustrative scenario to be mistaken for field measurement.