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Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (T max ), minimum temperature (T min ), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (T max , T min , and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of T max , T min , and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent for publication: Not applicable. Consent to participate: Not applicable. Ethics approval: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

This study focuses on a hydro-meteorological variable, streamflow, and its vulnerability to global warming. In this study, we analyze monthly streamflow in 40 catchments across four major South Indian River basins: Cauvery, Godavari, Krishna, and Mahanadi, within the Indian Peninsular region. The chosen catchments have available streamflow series records ranging from 41 to 49 years, spanning from 1970 to 2018. To uncover trends in the streamflow datasets, an innovative technique called Innovative Polygon Trend Analysis (IPTA) is used. IPTA detects trend transitions between successive time series parts and identifies possible trend pieces within a year, crucial for agricultural and irrigation practices. Results reveal diverse polygon patterns across different catchments, reflecting non-systematic streamflow variation. Non-systematic changes occur primarily in the Cauvery basin, followed by the Godavari, Krishna, and Mahanadi basins. Varied trends—increasing and decreasing—are dispersed across different catchments. Increasing trends were generally observed in the standard deviation of streamflow, while decreasing trends were more widespread in the mean of streamflow. Notably, the Godavari basin displays more prevalent decreasing trends in the majority of catchments for different months, followed by Cauvery and Krishna. Mahanadi basin has more catchments showing an increasing trend than other basins. The results of this study provide deeper insight into the streamflow dynamics within these four basins. [ABSTRACT FROM AUTHOR]

Climate change (precipitation and temperature) has significantly affected the hydrological regimes and future climate projection. Integration of climate model with physical based model is crucial for quantitative measurement of changes in surface water regime. For accurate estimation, modelling framework need finer scale resolution of climate model output. In this study, we examined the bias corrected, statistically downscale models drawn from the NASA, Earth Exchange Global Daily Downscaled Projections–Coupled Model Intercomparison Project Phase 5 (NEX-GDDP-CMIP5) over the study region. The rainfall and temperature projection output from the INMCM-4, MRI-CGCM3 and their ensemble mean performed well over the Mahi River basin (MRB), India. In this study, the climate data integrated with the SWAT model to analyse the potential impact of climate change on the discharge of MRB. The finding indicates that in the near future (2011–2040) projection of annual average streamflow increases by 76.74% based on the INMCM-4 outputs, 25% based on the MRI-CGCM3 outputs, and 24.53% based on the ensemble mean in comparison to the baseline period (1981–2010). Further, the modelling results of mean monthly streamflow in rainy season indicated that the lowest and highest streamflow changes will be ranging from about 631.07–2718.42 m3/s as observed by INMCM-4, 491.71–2938 m3/s observed by MRI-CGCM3, 513.02–2270.18 m3/s observed by ensemble mean, in the near future. Similarly, in the summer season, the lowest level of stream flow is found to be 158.27 m3/s observed by MRI-CGCM3, 193.38 m3/s (ensemble mean) and 258.53 m3/s (INMCM-4), respectively. Additionally, the streamflow trend was assessed by Mann–Kendall and Sen's slope method at the monthly, seasonal and annual scales. The future streamflow projection represented the ascending trend observed in south west and winter monsoon, while the descending trend was observed in pre-monsoon and post-monsoon under the INMCM-4, MRI-CGCM3, and ensemble mean. Results on projected precipitation, temperature and streamflow accretion would help to develop effective adaptation measures for reducing the impacts of climate change and to work out long-term water resource management plans in the river basin. [ABSTRACT FROM AUTHOR]