Meteorological droughts abetted by hot events could instigate an agricultural drought that eventually affects crop yield. Different types of droughts may coexist or occur in succession. A single index based on one particular variable may not be sufficient to quantify such compound drought events. Therefore, this study embedded drought indexes ofstandardized precipitation index (SPI), standardized soil-moisture index (SSI), and standardized temperature index (STI) with Gaussian copula functions to study compound agrometeorological drought and hot events in India from 1948 to 2014. By standardizing the joint probability of the SPI, SSI, and STI time series, the standardized compound drought and hot index (SCDHI) was developed. The SCDHI values in the monsoon months of different climatic zones have a strong correlation of about 0.95 with other well-established indexes such as the standardized compound event indicator (SCEI), which incorporates SPI and STI, and the multivariate standardized drought index (MSDI), which incorporates SPI and SSI. Based on the areal extent, 1965–1966, 1972, 1987, and 2002 were identified as significant compound drought years in India. The index also identified three successive compound events of the 2012–2014 northest monsoon in the southern peninsular region. A notable increase in the frequency of compound drought and hot events was found post-2000. The case studies of the major drought events and the dependent pattern of SCDHI on its constituent indexes indicate that SCDHI performs well as an indicator of compound agrometeorological and hot events across different climatic regions and in both southwest and northeast monsoons.

The conventional method of univariate flood frequency analysis based solely on peak flow ($Q$ ) overlooks the influence of other characteristic flood variables, such as the accumulated volume ($V$ ) of the flood and the duration ($D$ ) of flood events. A copula-based multivariate model that represents the joint behavior of these dependent flood variables could aid in computing joint return periods of flood events in tropical, seasonal rivers of India. In connection with the potential locations of high flood risk among west-flowing rivers, multivariate flood frequency analysis was performed on the Bharatapuzha, Periyar, and Chaliyar Rivers of the state of Kerala, India. A comparison of univariate return periods with multivariate return periods reveals that the intersection of flood variables corresponding to a 20-year univariate return period yields a trivariate return period of $91\mathrm{years}$ at Bharatapuzha and $144\mathrm{years}$ at Periyar and Chaliyar. The return period by the union of such flood variables is $10\mathrm{years}$ . The choice of flood variables and their combination depend on the problem at hand. Additionally, basinwise confluence flood frequency models are built with the peak flow at each stream as the random variables show their spatial interdependencies using conditional probabilities and return periods. The copula-based flood coincidence risk model captures the temporal aspect of the co-occurrence of flood peaks in a basin’s streams. The co-occurrence of annual flood peaks between the stream pairs of the Bharatapuzha, Periyar, and Muvathapuzha basins is the highest toward the end of July with probabilities of approximately $2.2\times {10}^{-4}$ (at the Kumbidi and Mankara stations), $3\times {10}^{-4}$ , and $1\times {10}^{-3}$ , respectively. A trio of copula-based multivariate flood frequency, confluence flood frequency, and flood coincidence risk models could be used to design safe and economic hydrologic infrastructure.

This study examines future drought propagation (the temporal transition from meteorological to agricultural droughts), persistence (inter-seasonal agricultural droughts), and spatial concurrence (simultaneous occurrence of monsoonal agricultural droughts across regions) under climate change using a multivariate copula approach in monsoon-dominant Asia. The standardised precipitation index (SPI) and standardised soil moisture index (SSI) are used to analyse meteorological and agricultural droughts, respectively. Under the worst-case emission scenario (Shared Socioeconomic Pathway, SSP5-8.5), South Asia (excluding western and peninsular India) and eastern China are projected to experience intensified drought propagation compared to in the historical period (1975–2014). In addition to increased propagation in these regions, the propagated agricultural droughts are expected to persist across seasons in the future. On the hydrologically significant Tibetan Plateau, all-season droughts that were historically rare, with return periods exceeding 50 years, could occur as frequently as once every 5 years in the far-future period (2061–2100). Random forest models indicate that the temperature is a key driver of future agricultural droughts in nearly half of the study area. The increasing non-rainfall-related agricultural droughts in the far future could be attributed to the rise in temperature. Based on bivariate return periods of spatial concurrence, frequent future spatial drought concurrence is anticipated between populous South Asia and East Asia compared to the historical time frame, posing risks to water and food security. Conversely, Southeast Asia is projected to experience reduced spatial drought concurrence with other regions, which could encourage greater regional cooperation. Overall, this comprehensive approach, which integrates three aspects of drought dynamics, offers valuable insights for climate change mitigation, planning, and adaptation.