The study, published in Theoretical and Applied Climatology, analyzes 124 years of daily rainfall observations, from 1901 to 2024, using the India Meteorological Department’s gridded dataset at a resolution of 0.25 degrees of latitude and longitude. Rather than pooling all extreme rainfall days together, the researchers separated the annual maximum one-day rainfall totals into two climatologically distinct seasons: the southwest monsoon from June to September, known in the region as JJAS, and the northeast monsoon from October to December, abbreviated OND. This separation matters because Tamil Nadu sits in the rain shadow of the Western Ghats during the summer monsoon, while the retreating northeast monsoon, fueled by moisture-laden easterly winds and tropical cyclones in the Bay of Bengal, delivers the state’s most destructive downpours.

The statistical machinery behind the analysis is a Bayesian hierarchical generalized extreme value model, or GEV model, fitted with the INLA-SPDE computational approach. The GEV distribution is the standard tool of extreme value theory: it describes the distribution of block maxima, in this case the largest single-day rainfall total in each season of each year, using three parameters that control the location, scale, and shape of the tail. What makes the Bayesian hierarchical framework powerful is that these parameters are allowed to vary smoothly across space, linked through a latent Gaussian field approximated by the stochastic partial differential equation method, so that neighboring grid cells inform one another and uncertainty is propagated coherently across the entire state. The result is not a single statewide estimate but a map of return levels, each accompanied by a full posterior distribution that quantifies how confident the model is at every location.

The headline finding is a dramatic seasonal asymmetry. At the median of the distribution, one-day rainfall maxima during the northeast monsoon are approximately 1.4 times larger than those of the southwest monsoon. In the upper tail, where the most dangerous events live, the asymmetry widens to a factor of 1.8. The spatial median 50-year return level, the rainfall depth expected on average once in 50 years, is 123.1 millimeters per day for the southwest monsoon but 220.5 millimeters per day for the northeast monsoon. In other words, the season that most engineering studies have historically treated as the primary hazard for peninsular India is, for Tamil Nadu, the milder of the two. The northeast monsoon, shorter and less famous, is unambiguously the state’s dominant extreme-rainfall engine.

One of the study’s most methodologically interesting choices is its treatment of nonstationarity. Many recent studies impose time trends or climate covariates on extreme rainfall models as a matter of course, assuming that a warming world has already shifted the distribution. Here, the authors took a more disciplined approach: nonstationarity in the location parameter, expressed through time, the El Niño-Southern Oscillation, and the Indian Ocean Dipole, was treated as a testable model feature rather than an assumption. Covariates were only retained if the data and validation metrics supported them. This matters because overfitting a trend to 124 years of noisy extremes can produce return levels that look climate-informed but perform poorly out of sample.

The validation strategy is unusually thorough for a regional frequency analysis. Temporal hindcast validation tested whether the model, fitted to earlier portions of the record, could predict extremes in later periods. Spatially blocked cross-validation removed contiguous regions from the fit and asked how well the model transferred to them, a sterner test than leaving out isolated points because it probes whether the spatial structure generalizes. Pre-specified sensitivity analyses, including a late-epoch risk-inflation test restricted to 1961 through 2024, examined whether the full-record estimates might understate present-day risk. These checks revealed genuine limitations. The southwest monsoon tail was found to be under-calibrated: the nominal 50-year level was exceeded at an empirical rate of 0.029, corresponding to an effective return period of only about 34 years. And spatial transferability was regionally heterogeneous, particularly for the northeast monsoon, meaning the maps are more reliable in some districts than others.

The stationarity question produced the study’s most nuanced conclusion. Across the full 124-year record, risk inflation factors, ratios comparing nonstationary or recent-period estimates to the stationary benchmark, came out close to unity, suggesting no dramatic long-term shift. But when the analysis was restricted to the late epoch from 1961 onward, the inflation factors rose, with median 50-year risk inflation values of 1.076 for the southwest monsoon and 1.038 for the northeast monsoon. The authors’ interpretation is careful and worth emphasizing: the full-record stationary result should be read as a benchmark, not as definitive evidence that historical design standards remain adequate today. A seven to eight percent inflation in the recent period may sound modest, but for infrastructure with a design life of 50 to 100 years, even small shifts in the tail compound into materially different flood probabilities.

Why does this matter beyond the statistics? Tamil Nadu has repeatedly experienced catastrophic urban flooding, most visibly in Chennai, where the northeast monsoon of 2015 delivered record-breaking deluges that paralyzed the metropolis. Frequency analyses that blend the two monsoons, or that borrow return levels calibrated for summer-monsoon-dominated regions of India, would systematically underestimate the depth of the design storm that the state’s drainage and reservoir systems must withstand. The finding that the northeast monsoon tail is nearly twice as heavy as the southwest monsoon tail provides a quantitative, uncertainty-quantified basis for seasonally differentiated design criteria, and it aligns with a growing body of evidence that widespread extreme precipitation during the northeast monsoon over south peninsular India has been increasing.

The study is also candid about what its outputs are not. Because the analysis rests on 0.25-degree gridded data, which averages rainfall over cells roughly 25 to 28 kilometers across, the resulting return-level surfaces represent areal-equivalent extremes rather than point rainfall at a specific gauge. Gridded products tend to smooth out the most intense convective cells, so the maps should be understood as spatially coherent hazard-screening surfaces, useful for identifying where risk is concentrated and where station-level analysis is most urgently needed, rather than as ready-made engineering inputs. The authors explicitly recommend local gauge or catchment-scale verification before any design use, a caveat that reflects a broader tension in climate science between the spatial completeness of gridded datasets and the fidelity of point observations.

What the study ultimately delivers is a template. By separating climatologically distinct seasons, treating nonstationarity as a hypothesis to be tested rather than a narrative to be assumed, validating with hindcasts and blocked cross-validation, and publishing uncertainty alongside every estimate, it models the kind of rigor that regional extreme-rainfall assessments will need as climate change reshapes the tails of the distribution worldwide. For Tamil Nadu, the message is concrete: the state’s flood future is written in October through December, and any risk framework that ignores the asymmetry between its two monsoons is measuring the wrong storm.

Subject of Research: Seasonal asymmetry in extreme daily rainfall and Bayesian spatial return-level estimation over Tamil Nadu, India

Article Title: Seasonal asymmetry in extreme daily rainfall over Tamil Nadu: Bayesian spatial return levels and a stationarity benchmark

Article References: Patra, T. R., Pathy, A. C., Sk, M. M., Safder, A., & Khatun, R. (2026). Seasonal asymmetry in extreme daily rainfall over Tamil Nadu: Bayesian spatial return levels and a stationarity benchmark. Theoretical and Applied Climatology, 157(11), Article 699. https://doi.org/10.1007/s00704-026-06618-9

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06618-9

Keywords: Tamil Nadu, extreme rainfall, northeast monsoon, southwest monsoon, return levels, generalized extreme value, Bayesian hierarchical model, INLA-SPDE, nonstationarity, ENSO, Indian Ocean Dipole, flood risk