This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions.

Accurate information of crop water requirements is essential for optimal crop growth and yield. Assessing this information at the appropriate time, particularly during the vegetative and reproductive stages when water demand is highest, is crucial for successful crop production. Our study cantered on the drought-prone Marathwada region, specifically targeting the years 2015 to 2020, encompassing the challenging drought year of 2015 and the favourable year of 2020. The crop water stress was detected using crop water stress (CWSI) index and compared with normalized difference vegetation index (NDVI) and normalized difference wetness index (NDWI) derived from satellite data. Our findings reveal a negative correlation between the CWSI and satellite derived vegetation indices NDVI and NDWI. Notably, the NDWI index exhibits stronger alignment with CWSI compared to NDVI. The correlation demonstrates particular robustness during drought or deficient rainfall years such as 2015, 2017, and 2019, while weaker correlations are observed in 2016, 2018, and 2020. Moreover, these correlations display variations across different areas within distinct rainfall zones.

This study presents a comprehensive analysis of the influence of ENSO events on soil moisture, rainfall, and soybean crop yields, alongside an evaluation of trends and their correlations with soybean crop yield. Marathwada and Vidarbha regions, contributing 39% of India's soybean production, were found to be highly susceptible to El Niño events especially western and central parts of this region. El Niño negatively affected crop yield, with reductions ranging from − 12% to -70%, including − 7% to -40% rainfall decreases and − 2% to -6% lower soil moisture. During a very strong El Niño year, Marathwada experienced a 40% rainfall deficit and a 70% crop yield decline, while Vidarbha had a 7% rainfall deficit and a 57% crop yield reduction. Soil moisture trends indicated declining levels in Vidarbha during critical soybean growth months (June and August), whereas Marathwada showed no significant trend. Rainfall trends revealed an increase in September in Marathwada, affecting crop maturity, while Vidarbha had a beneficial rainfall trend in July, promoting crop growth and soil moisture. Correlations between soil moisture, rainfall, and soybean yield varied, with Marathwada exhibiting correlations of 0.58 for soil moisture and 0.56 for rainfall, and Vidarbha displaying a correlation of 0.29 for soil moisture and 0.53 for rainfall.

Accurate and timely information of evapotranspiration (ET₀) is essential for multiple agricultural applications, including irrigation scheduling, studying crop-specific water loss at different growth stages, predicting crop yields, and forecasting drought conditions. The aim of this study is to examine the spatiotemporal patterns of ET₀ and facilitate the monitoring of crop water demand, optimizing irrigation water usage, and enhancing agricultural advisory services. This paper estimates regional-level daily ET₀ gridded data with a spatial resolution of 12.5 km by integrating observed weather data, IMD GFS-T1534 reanalysis data, and INSAT-3D satellite-based insolation product using the standard FAO56 Penman-Monteith empirical equation. The estimated monthly mean of ET₀ across India ranged from 10 to 400 mm. ET₀ exhibited an increasing trend from January to May, reaching its maximum values in May. In June, ET₀ significantly decreased as the monsoon arrived, coinciding with the movement of rainfall patterns. The month of December exhibited the lowest ET₀ values. The estimated daily gridded ET₀ was compared with station-based ET₀, resulting in daily correlation coefficient R² and daily maximum absolute percentage errors ranging from 0.34 to 0.90 and 10% to 27% respectively. However, these errors decreased to a large extent when considering multiday accumulated values. A comparison was conducted between the GLDAS model ET₀ and the station-estimated values, revealing an overestimation of ET₀ by the GLDAS model. Additionally, significant variations were observed among the meteorological subdivisions. This highlights the necessity for proper calibration of the GLDAS model ET₀ or its effective agricultural application.