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Groundwater in north India remains a vital food and water security resource for more than one billion people. Both summer monsoon drying, and winter warming pose considerable challenges for rapidly declining groundwater. However, their impacts on irrigation water demands and groundwater storage under the observed and projected future climate remain unexplored. Using in situ observations, satellite data, and a hydrological model that considers the role of irrigation and groundwater pumping, we show that summer monsoon drying and winter warming accelerate groundwater depletion in north India during the observed climate, which will continue in the projected future climate. Summer monsoon precipitation has significantly (P‐value = 0.04) declined (∼8%) while winters have become warmer in north India during 1951–2021. Both satellite (GRACE/GRACE‐FO) and hydrological model‐based estimates show a rapid groundwater depletion (∼1.5 cm/year) in north India with a net loss of 450 km3 of groundwater during 2002–2021. The summer monsoon drying followed by winter warming cause a substantial reduction in groundwater storage due to reduced groundwater recharge and enhanced pumping to meet irrigation demands. Summer monsoon drying and winter warming will continue to affect groundwater storage in north India in the future. For instance, summer monsoon drying (10%–15% deficit for near‐far periods) followed by substantial winter warming (1–4°C) in the future will further accelerate groundwater depletion by increasing (6%–20%) irrigation water demands and reducing groundwater recharge (6%–12%). Groundwater sustainability measures including reducing groundwater abstraction and enhancing the groundwater recharge during the summer monsoon seasons are needed to ensure future agricultural production. Plain Language Summary: Groundwater in north India is facing rapid depletion due to summer monsoon drying and winter warming. The decline in summer monsoon precipitation and warmer winters have led to major droughts, resulting in a massive loss of groundwater between 2002 and 2021. Summer monsoon drying coupled with winter warming climate will increase irrigation water demands and reduce groundwater recharge. This necessitates the implementation of groundwater sustainability measures to ensure future agricultural production during monsoon droughts. Key Points: Examined the role summer monsoon drying and winter warming on groundwater storage in IndiaRapid groundwater depletion (∼1.5 cm/year) resulted in a net loss of 450 km3 of groundwater during 2002–2021Summer monsoon drying followed by substantial winter warming under the warming climate will further accelerate groundwater depletion [ABSTRACT FROM AUTHOR]

Precipitation plays a significant role in human society and the environment, and how large‐scale climatic features influence precipitation has obtained worldwide attention. The nonhomogeneous hidden Markov model (NHMM) is a typical method to downscale large‐scale climatic elements to the regional level for many hydrologic applications. The traditional NHMM using point estimates of parameters has the risk to have no solutions for parameter estimation, but the Bayesian‐NHMM provides a Bayesian method to estimate parameters and avoids the risk. In this study, the suitability of the Bayesian‐NHMM in East Asia is evaluated. Two typical regions (i.e., the Yangtze River basin and the Zhujiang River basin in China) dominated by different climates were chosen to check model performance. Results show that: (1) the model could divide rainy‐season precipitation into several hidden states, whose variation is correspondent to the variability of monsoon and flux moisture transportation; (2) the model captures seasonality and inter‐annual variation of precipitation amount and also wet days during rainy season well in both river basins. These results suggest that the model could improve the prediction of daily precipitation in East Asia, which in turn could help many regions with similar climatic conditions worldwide to supervise floods and droughts. [ABSTRACT FROM AUTHOR]

The space–time fields of rainfall during a hurricane and tropical storm (TC) landfall are critical for coastal flood risk preparedness, assessment, and mitigation. We present an approach for the stochastic simulation of rainfall fields that leverages observed, high-resolution spatial fields of historical landfalling TCs rainfall that is derived from multiple instrumental and remote sensing sources, and key variables recorded for historical TCs. Spatial realizations of rainfall at each time step are simulated conditional on the variables representing the ambient conditions. We use 6 hourly precipitation fields of tropical cyclones from 1983 to 2019 that made landfall on the Gulf coast of the US, starting from 24 h before landfall until the end of the track. A conditional K-nearest neighbor method is used to generate the simulations. The TC attributes used for conditioning are the preseason large-scale climate indices, the storm maximum wind speed, minimum central pressure, the latitude and speed of movement of the storm center, and the proportion of storm area over land or ocean. Simulation of rainfall for three hurricanes that are kept out of the sample: Katrina [2005], Rita [2005], and Harvey [2017] are used to evaluate the method. The utility of coupling the approach to a hurricane track simulator applied for a full season is demonstrated by an out-of-sample simulation of the 2020 season. [ABSTRACT FROM AUTHOR]