The study, led by Deepti Tandel with Mani Kant Verma and M.K. Verma, set out to quantify how large-scale ocean-atmosphere interactions shape regional monsoon variability at a scale that matters for farmers and water managers. Climate change is increasingly altering rainfall patterns, and understanding the fingerprints of ENSO and the IOD on district-level rainfall, monsoon onset, and wet-dry conditions provides a scientific basis for drought monitoring and agricultural planning. The team assembled daily rainfall data for the monsoon season across the full 65-year record, then applied a battery of statistical tools designed to detect trends, classify drought severity, and test associations with ocean indices.

Methodologically, the researchers used the Modified Mann-Kendall test, which corrects for autocorrelation in time series, together with Sen’s slope estimator to quantify the magnitude of trends in rainfall. To characterise drought and wetness, they computed the Standardised Precipitation Index (SPI), a widely used metric that expresses rainfall anomalies in units of standard deviation, with values below zero indicating drier-than-normal conditions and values above zero indicating wetter-than-normal ones. Associations with ocean conditions were tested using both Pearson and Spearman correlation analyses, while differences among groups were evaluated with the Kruskal-Wallis test and Dunn’s post-hoc comparisons. This combination of non-parametric and correlation-based techniques allowed the team to assess trends and teleconnections robustly across districts with differing rainfall regimes.

The results paint a picture of substantial spatial variability. District-level SPI values ranged from approximately −2.5 to +3.0, spanning severe drought to pronounced wetness within a single state. The analysis identified 15 major drought years: 1965, 1966, 1968, 1974, 1979, 1982, 1986, 1991, 1997, 2002, 2009, 2013, 2015, 2017, and 2023. In contrast, seven years stood out as major wet years: 1961, 1980, 1981, 1994, 2001, 2003, and 2025. During the major drought years, the mean SPI across districts ranged from −1.733 to −0.505, confirming that these events were widespread rather than localised, yet the spread of values also underscored how unevenly drought stress is distributed across the state.

Monsoon onset, a critical variable for sowing decisions in rainfed agriculture, varied considerably across the state, from 14 June in the earliest districts to 2 July in the latest. This three-week spread means that neighbouring districts can face very different planting windows in the same season, complicating one-size-fits-all agricultural advisories. The timing of onset was found to be significantly influenced by conditions in the preceding months: the March-April-May Oceanic Niño Index (MAM ONI), a measure of Pacific sea surface temperature anomalies, and the state of the IOD both had significant effects on rainfall variability and on when the monsoon actually arrived over individual districts.

The statistical link between the Pacific and Chhattisgarh’s rains was quantified directly. The association between ONI and monsoon rainfall was significant in both correlation frameworks, with Pearson’s r = 0.401 (p = 0.001) and Spearman’s ρ = 0.416 (p = 0.006). These moderate but highly significant coefficients indicate that a substantial fraction of year-to-year monsoon variability in the state is traceable to the phase of ENSO, a finding consistent with the broader literature on how Pacific sea surface temperature anomalies modulate the Indian summer monsoon through large-scale atmospheric circulation changes.

Perhaps the most striking result is the spatial pattern of emerging aridity. The districts of Mungeli, Kabirdham, Bilaspur, Balodabazar, and Surajpur experienced moderate to extremely dry conditions, while Bastar, Bijapur, Narayanpur, and Sukma, in the forested south of the state, showed a transition from extremely wet to moderately wet conditions. This shift indicates an emerging tendency toward dryness even in historically wetter districts, suggesting that the drying signal is not confined to the already arid margins but is creeping into areas whose farming systems and ecosystems were built around more abundant rainfall.

The authors attribute this increasing tendency toward aridity to the combined influence of ENSO, the IOD, and regional hydroclimatic processes. The IOD, a seesaw of sea surface temperatures between the western and eastern Indian Ocean, can reinforce or offset ENSO’s influence on Indian rainfall depending on the relative phases of the two modes. When both act in concert, their combined effect on monsoon circulation can be amplified, and the Chhattisgarh analysis suggests that such interactions leave detectable imprints on district-scale rainfall, onset timing, and drought severity across the 65-year record.

The practical implications are considerable. Chhattisgarh’s economy is heavily dependent on rainfed agriculture, and previous work by the same group has documented how dry and wet spells affect crop productivity in the state. By demonstrating that pre-monsoon ocean conditions carry usable information about subsequent rainfall variability and onset timing, the study provides a scientific foundation for district-level drought monitoring, seasonal agricultural planning, and water-resource management. The identification of specific drought-prone and wet-to-dry transition districts offers a spatially targeted basis for climate-resilient strategies, from crop choice and irrigation investment to contingency planning for delayed monsoons.

As climate change continues to reshape global precipitation patterns, studies of this kind highlight how global ocean oscillations and regional processes interact to determine the fate of individual farming districts. For Chhattisgarh, the message from six and a half decades of data is clear: the monsoon is becoming more uneven, drought years are a recurring feature of the record, and even the wettest corners of the state are drifting toward drier conditions. Translating that statistical evidence into early-warning systems and adaptive water management may prove essential for the millions of people whose livelihoods depend on the timing and abundance of the Southwest monsoon rains.

Subject of Research: Influence of ENSO and the Indian Ocean Dipole on Southwest monsoon rainfall variability and drought across Chhattisgarh, India

Article Title: Spatiotemporal variability of Southwest monsoon rainfall and drought in Chhattisgarh: associations with ENSO and IOD

Article References: Tandel, D., Verma, M. K., & Verma, M. (2026). Spatiotemporal variability of Southwest monsoon rainfall and drought in Chhattisgarh: associations with ENSO and IOD. Theoretical and Applied Climatology, 157(11), Article 728. https://doi.org/10.1007/s00704-026-06662-5

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06662-5

Keywords: monsoon, Chhattisgarh, ENSO, Indian Ocean Dipole, drought, Standardised Precipitation Index, rainfall variability, monsoon onset, climate change, trend analysis, India, agriculture