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