The research team drew on the Climate Hazards Group InfraRed Precipitation with Station data product, known as CHIRPS, which blends infrared satellite observations with ground-based rain gauge measurements to produce rainfall estimates at a resolution of roughly five kilometres. For each of thirteen administrative units, or mandals, within Warangal district, the researchers assembled monthly rainfall totals from 1990 to 2024 and aggregated them into annual series. The dataset proved complete, with no missing values requiring imputation, giving the team a clean thirty-five-year record for every station. All trend computations were carried out in Python, while ArcGIS was used to map the spatial patterns of rainfall, trend magnitudes, and homogeneity test results across the district.

Warangal is an instructive place to look for climate signals. Sitting on the Deccan Plateau at elevations between roughly 200 and 430 metres above sea level, the district occupies a transition zone between the dry interior plateau and the more humid eastern plains. Under the Köppen–Geiger classification it carries the label Aw, a tropical savanna climate with semi-arid character: hot summers, mild winters, and a single dominant rainy season. Between 80 and 90 percent of the annual total falls during the southwest monsoon from June to September, when moisture-laden winds sweep in from the Bay of Bengal. Mean annual temperatures hover between 26 and 28 degrees Celsius, and high potential evapotranspiration means that even modest deficits in rainfall translate quickly into agricultural stress, depleted groundwater, and shrinking tanks and reservoirs.

The descriptive statistics alone reveal a striking spatial gradient. Mean annual rainfall across the thirteen stations ranged from 977.91 millimetres at Khilla Warangal in the southwest to 1217.48 millimetres at Khanapur in the northeast — a difference of nearly a quarter. The wettest stations, in descending order, were Khanapur, Nekkonda, Chennaraopet, Nallabelly, and Narsampet, all clustered toward the northern and eastern parts of the district, while the driest were the urban-core stations of Warangal and Khilla Warangal. Interannual variability followed a similar geography: Nekkonda recorded a standard deviation of 221.04 millimetres, about 29 percent higher than Warangal’s 171.23 millimetres. Every station showed positive skewness, between 0.24 and 0.54, meaning that annual totals are disproportionately inflated by occasional very wet years rather than spread evenly across seasons — a statistical fingerprint of intensifying rainfall extremes.

To detect underlying trends, the team applied the Mann–Kendall test, a non-parametric method recommended by the World Meteorological Organization for climatological and hydrological series because it makes no assumption about the data’s distribution and is robust to outliers. The test works by comparing every observation with every subsequent one, summing the signs of the differences, and standardising the result against the expected variance under a no-trend null hypothesis. The results were unambiguous in direction: all thirteen stations showed positive Mann–Kendall statistics, indicating increasing rainfall. Eight stations — 61.5 percent of the total — reached statistical significance at the five percent level, with the strongest trends at Narsampet (Z = 2.443, p = 0.014), Nallabelly (Z = 2.244, p = 0.024), and Khanapur (Z = 2.159, p = 0.030). The remaining five stations showed positive but non-significant trends, suggesting their changes remain within the envelope of natural variability.

Quantifying the magnitude of those trends fell to Sen’s slope estimator, which computes the change between every possible pair of years, then takes the median of all pairwise slopes as the trend estimate. Because it is median-based, the estimator resists distortion by individual extreme years. The results showed rainfall intensifying everywhere, but at very different rates: from a low of 3.851 millimetres per year at Wardhannapet to a high of 7.945 millimetres per year at Narsampet. Nallabelly (7.264), Khanapur (7.191), Duggondi (7.012), and Sangem (6.700) followed closely. Kendall’s tau, a measure of the strength of the monotonic relationship, ranged from 0.163 to 0.291, peaking at Narsampet. Linear trendlines explained between 7.6 percent (Nekkonda) and 19.2 percent (Narsampet) of the year-to-year variance — modest figures that underscore how noisy monsoon rainfall remains even when a genuine long-term signal is present.

The study’s most distinctive contribution lies in its homogeneity analysis, which asks whether a rainfall series is drawn from a single stable distribution or has undergone abrupt step changes. Three complementary tests were deployed. Pettitt’s test, which splits a series into two segments and searches for the point where their distributions differ most, found that twelve of thirteen stations remained formally homogeneous, with only Narsampet showing a significant change point (p = 0.034) near the start of the record; Nallabelly and Khanapur came close to the threshold. The Standard Normal Homogeneity Test, which compares the mean of the first k observations with the mean of the remainder, flagged near-significant break points at ten stations, most commonly around 2018 to 2019. The Buishand Range test, based on cumulative deviations from the long-term mean, detected significant inhomogeneity at all thirteen stations, with Q values between 5.18 and 7.07, and a common break year around 2004 at most sites.

Comparing mean rainfall before and after the detected breaks made the regime shift tangible. Following the 2004 transition identified by Pettitt’s test, mean annual rainfall rose at every station, with notable jumps at Chennaraopet (from 1065.54 to 1214.28 millimetres), Duggondi (1019.35 to 1165.45), Khanapur (1127.83 to 1284.71), and Narsampet (1108.86 to 1281.14). The SNHT-based breaks around 2019 told a similar story: Geesugonda’s mean climbed from 993.70 to 1215.35 millimetres, Sangem’s from 1027.35 to 1270.36, and Khanapur and Nallabelly each gained more than 200 millimetres across their respective break intervals. The Buishand analysis recorded the largest post-shift increases at Narsampet (1121.14 to 1484.30 millimetres) and Nallabelly (1144.71 to 1378.51), though Wardhannapet showed a slight post-break decline. Taken together, the three tests converge on the same conclusion: Warangal’s rainfall is non-stationary, reorganised by structural changes rather than drifting smoothly.

The authors attribute the pronounced northeast-to-southwest rainfall gradient to a combination of orographic effects, monsoon moisture transport from the Bay of Bengal, and local land–atmosphere interactions, consistent with patterns documented across peninsular India. The positive skewness and elevated kurtosis at stations such as Raiparthy, Narsampet, Khanapur, and Parvathagiri suggest that annual totals increasingly depend on a handful of intense downpours — a pattern that echoes broader findings that short-duration rainfall extremes are intensifying across the Indian subcontinent as the atmosphere warms. Where trends were statistically insignificant, the researchers point to the continuing dominance of natural climate oscillations, notably the El Niño–Southern Oscillation and the Indian Ocean Dipole, which modulate monsoon strength and timing from year to year. The break years of 2004 and 2018–2019 align with periods of documented shifts in Indian monsoon behaviour and rising extreme-rainfall frequency reported in earlier regional studies.

For a district whose agriculture, drinking water supply, and groundwater recharge all hinge on the monsoon, the implications cut both ways. More total rainfall could replenish aquifers and reservoirs, but if it arrives in fewer, heavier bursts it is more likely to run off rapidly, driving floods, erosion, and reduced infiltration — a paradox familiar from other monsoon-dominated regions. The study’s integrated approach, pairing trend detection with multiple homogeneity tests at the district scale, is presented by the authors as the first such assessment for Warangal, and it carries practical weight: reservoir operation rules, hydrological design standards, and crop calendars calibrated to a stationary climate may all need revision. The authors caution that their analysis rests on a single satellite-gauge blended dataset and statistical trend detection; future work incorporating temperature, evapotranspiration, soil moisture, and climate indices with advanced modelling will be needed to pin down the mechanisms. But the headline finding stands: Warangal is shifting toward a wetter, more volatile rainfall regime, and the shift has been abrupt enough to redraw the district’s hydrological baseline within a single generation.

Subject of Research: Long-term rainfall trend detection and homogeneity analysis in Warangal district, Telangana, India

Article Title: Assessing long-term rainfall trends using non-parametric approach in Warangal district, Telangana, India

Article References: Kondagadupula, M., Pasha, M. A., M.A, M. A., N, H., M, A., & Sabinikari, S. K. (2026). Assessing long-term rainfall trends using non-parametric approach in Warangal district, Telangana, India. Discover Geoscience, 4(1), Article 391. https://doi.org/10.1007/s44288-026-00758-1

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00758-1

Keywords: rainfall variability, Mann–Kendall test, Sen's slope estimator, Pettitt test, SNHT, Buishand Range test, CHIRPS, monsoon, Telangana, climate change, hydrology, non-stationarity

Atmospheric evaporative demand sits at the hinge of the terrestrial water balance. It governs the moisture exchange between land and air, determines how much of a given rainfall episode is lost back to the sky, and in water-limited regions it is tightly coupled to the onset and intensification of drought. When demand rises faster than supply, soils dry, reservoirs shrink and vegetation stress compounds even if precipitation itself does not collapse. Yet despite this central role, the multi-decadal behaviour of ET0 and its structural changes have remained poorly characterised, particularly in semi-arid regions where the question of whether atmospheric conditions are statistically associated with their impact on water balance variability has not been systematically answered. The Tamil Nadu study set out to close that gap by examining long-term hydroclimatological behaviour through four lenses: spatial heterogeneity, temporal non-stationarity, atmospheric control and impacts on the climatic water balance across contrasting hydro-climatic regimes.

The evidence base was drawn from the NASA POWER database, a publicly accessible satellite-derived and modelled meteorological record that provided temperature, relative humidity, solar radiation, wind speed and precipitation for the period 1985 to 2024. Rather than relying on a single station, the researchers selected representative stations spanning the diverse hydro-climatic regimes of Tamil Nadu, from the wetter coastal and western zones to the drier interior plains. This design matters because a state-level average can easily mask the fact that neighbouring districts may be drifting in opposite directions. By treating each regime on its own terms, the analysis could reveal whether the drivers of evaporative demand behave uniformly or whether their dominance changes with the local climate setting.

Methodologically, the study leaned on a battery of robust non-parametric techniques chosen for their resilience to outliers and to the non-normal distributions typical of hydro-climatic data. Trend detection used the Mann–Kendall test paired with Sen’s slope estimator, a combination that has become a standard for identifying monotonic change in environmental series. The pivotal innovation, however, was the Pettitt test for regime shift analysis, which searches for a single abrupt change point in a time series rather than assuming gradual change. Extreme event analysis characterised the behaviour of ET0 at the tails of the distribution, where agricultural stress concentrates, and a SARIMA model, a seasonal autoregressive integrated moving average framework, was used to forecast short-term ET0 dynamics. Finally, a driver dominance analysis quantified the relative contribution of individual atmospheric factors to ET0 variability, allowing the team to rank the controls rather than merely list correlations.

The headline finding is that ET0 in Tamil Nadu does not follow a simple, monotonic trajectory. Instead, the records show statistically significant regime changes, meaning that the series jumps between quasi-stable states with different mean levels and different atmospheric controls. The interaction between temperature, radiation and humidity emerges as the governing mechanism, and the balance of power among these three variables shifts from one hydro-climatic regime to another. In practical terms, this means that a warming trend does not translate into a uniform increase in atmospheric thirst everywhere. Where humidity is high, rising temperatures may be partially offset by the suppression of evaporation; where the air is already dry, the same warming can push demand sharply upward. The study’s driver dominance analysis makes this regime dependence explicit, showing that the atmospheric control on ET0 varies systematically under different hydro-climatic conditions.

Extreme ET0 behaviour and the response of the climatic water balance also diverged across regimes, a pattern the authors interpret as the spatial fingerprint of climatic water stress. In the drier interior, where rainfall is marginal and evaporative demand is chronically high, shifts in ET0 translate almost directly into deeper water deficits, because there is little buffer between supply and demand. In wetter zones, the same shifts may be absorbed by soil moisture and surface storage, at least temporarily. This asymmetry has a sobering implication: identical large-scale climate signals can produce radically different drought outcomes depending on where they land. Water planners who rely on state-wide or basin-wide averages risk misjudging both the severity and the geography of emerging stress.

Perhaps the most consequential conclusion of the paper is a methodological warning. Atmospheric evaporative demand, the authors argue, cannot be understood on the basis of monotonic trends alone, because its spatial heterogeneity, temporal non-stationarity and regime dependence violate the assumptions underlying simple trend analysis. A Mann–Kendall test applied to a series that has jumped between two regimes may report a significant trend that is really an artefact of a step change, or conversely may miss genuine change that is concentrated in a short transition. For predictability, this reframing is critical. Forecast systems and drought early-warning schemes that extrapolate a linear trend will systematically misjudge the future if the underlying process is one of regime dynamics. Recognising the regime structure, by contrast, opens the door to forecasts conditioned on the current state, which is precisely what the SARIMA component of the study begins to explore for short-term ET0 dynamics.

The findings arrive amid a broader scientific reassessment of global evaporative demand. Recent work has documented that climate change has increased evaporative demand across most of the planet, with South Asia standing out as a notable exception in some global assessments, and researchers have begun naming prolonged episodes of extreme atmospheric demand, such as so-called thirstwaves, as a distinct class of agricultural hazard. The Tamil Nadu results add regional texture to this global picture, showing that even within a single Indian state the trajectory of atmospheric thirst is not uniform. They also echo a long-standing puzzle in hydrology, the so-called evaporation paradox, in which observed pan evaporation can decline even as temperatures rise, a reminder that humidity, radiation and wind interact with temperature in ways that defy single-variable intuition.

For a state where agriculture consumes the bulk of freshwater and where monsoon failures routinely trigger drinking-water emergencies, the practical stakes are considerable. Knowing which atmospheric variable dominates ET0 in each regime tells irrigation authorities what to watch: humidity and radiation in some zones, temperature in others. Knowing that regimes shift abruptly rather than drift smoothly suggests that water budgets should be revised at detected change points rather than on fixed assumptions. The availability of the underlying NASA POWER data, covering 1985 to 2024, and of the processed analytical outputs on reasonable request from the corresponding author, means the framework can be replicated and extended to other semi-arid regions facing similar questions. The research was supported by the Tamil Nadu Chief Minister’s Research Grant, reflecting state-level interest in the predictability problem.

Ultimately, the study reframes atmospheric evaporative demand from a passive consequence of warming into an active, regime-governed component of the climate system with its own predictability structure. In semi-arid Tamil Nadu, the atmosphere’s thirst arrives in steps, not slopes, and each step rewrites the local water balance in a different way. Capturing that step-like behaviour, the authors contend, is the key to anticipating drought before it takes hold, and to managing a water future in which the demand side of the equation may change faster than the supply side ever will.

Subject of Research: Multi-decadal variability and regime shifts in atmospheric evaporative demand and climatic water balance in Tamil Nadu, India

Article Title: Multi-decadal variability and regime dynamics of atmosphere evaporative demand and climatic water balance in Tamil Nadu, India: implications for predictability

Article References: Multi-decadal variability and regime dynamics of atmosphere evaporative demand and climatic water balance in Tamil Nadu, India: implications for predictability. (n.d.). https://doi.org/10.1007/s00704-026-06592-2

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06592-2

Keywords: evaporative demand, reference evapotranspiration, ET0, Tamil Nadu, drought, regime shift, Pettitt test, Mann-Kendall trend analysis, SARIMA forecasting, climatic water balance, semi-arid climate, NASA POWER