Agriculture is highly sensitive to interannual climate variability, particularly in regions dominated by rainfed systems. This study investigates how spatio‑temporal variability in rainfall and temperature influences district‑level crop productivity using only secondary datasets and a reproducible GIS‑enabled workflow. We demonstrate a regional analysis framework (illustrated for Maharashtra State, India; easily adaptable to other regions) combining (i) gridded climate surfaces, (ii) satellite‑derived vegetation indices, and (iii) official crop statistics. After harmonizing datasets to a common spatial unit (district) and temporal unit (season/year), we derive climate anomaly metrics (e.g., standardized precipitation index—SPI, temperature extremes), vegetation dynamics (NDVI/EVI), and agricultural outcomes (yield and area for major kharif and rabi crops). Panel regressions with district and year fixed effects quantify associations between climate variability and productivity while controlling for irrigation intensity and technology trends. Results indicate significant negative associations between warm‑season maximum temperature anomalies and yields of rainfed cereals and pulses, with rainfall variability exerting crop‑specific effects. Vegetation indices mediate part of the climate–yield relationship, and irrigation buffers climate shocks. The paper provides open, replicable methods, detailed data dictionaries, and GIS steps suitable for policy analysis and for extension to climate‑resilient planning.
