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Future global increases in the duration and severity of hydrologic drought present an emerging challenge for water resource management. However, projected changes to drought within global climate models are often complex, including potentially co‐occurring changes to the timing, duration, and severity of drought. Here, we apply a storyline approach for interpreting projections of future hydrologic drought to identify coherent narratives that include runoff trends, shifts in the seasonal drought timing, increases in multi‐year drought frequency, and increased drought severity within the Coupled Model Intercomparison Project Phase 6. We develop a framework to classify future drought storylines (2015–2100) and quantify model consensus to determine the most‐likely “dominant” storyline under four emission scenarios Shared Socioeconomic Pathways (SSPs) 1–2.6, 2–4.5, 3–7.0, and 5–8.5. Under a low‐emission scenario (SSPs 1–2.6) approximately one third of the land‐area is projected to be impacted by a dominant storyline of minimally detectable runoff trend paired with increased frequency of multi‐year drought. However, under the highest‐emission scenarios (SSPs 5–8.5), the most likely storyline shifts to an increase in multi‐year drought frequency, increased severity of drought, and negative long‐term runoff trends for 62% of the area in those same regions. Shifts in the seasonal timing of drought are a component of dominant storylines for the northern latitudes across all emission scenarios. These results provide an alternative mode of interpretation of co‐occurring changes to the features of future drought, framed in a way to support regional adaptation strategies to mitigate future drought impacts. Plain Language Summary Future hydrologic droughts are projected to become more severe and prolonged through the 21st century, posing challenges for water resource management across the globe. To understand these changes, we analyzed and ensemble of global climate models, focusing on runoff trends, shifts in seasonal drought timing, increased multi‐year drought frequency, and severity. Using a storyline approach, we identified coherent narratives for future drought scenarios from 2015 to 2100 under different emission scenarios. Under low‐emission scenarios, about one‐third of the land may experience minimal runoff changes but increased multi‐year drought frequency. However, under high‐emission scenarios, many global regions are projected to experience widespread multi‐year droughts, intensified drought severity, and decreased runoff. Furthermore, seasonal shifts in drought timing were prominent in northern latitudes across all future scenarios. These findings offer insights to support regional adaptation strategies to address future drought impacts. Key Points Future storylines that include more frequent multi‐year drought occur for at least 23% of the land‐surface in all future emission scenarios Seasonal shifts in the timing of drought occur in the dominant storyline for the northernmost latitudes regardless of emission scenario Hydrologic drought storylines that will most acutely stress water resources become more extensive under higher emission scenarios

The interaction between the Loop Current System (LCS) and the Gulf of Mexico (GoM)'s freshwater circulation is investigated during flood and drought years. We combine satellite‐derived sea surface salinity (SSS), ocean currents, and river discharge rates to examine patterns of salinity distribution throughout the entire GoM basin. Saline waters were reported intruding up the Mississippi River during drought years, so a close examination of these years is conducted. The relationship between the Loop Current (LC) and freshwater distribution are further investigated using a stepwise linear regression model. The relative impacts of river discharge and surface advective freshwater fluxes are quantified, and it is concluded that basin‐wide SSS depends significantly on the discharge from the Atchafalaya River, which leads by 4 months. Interannual salinity variability in the GoM is found to depend on the combination of discharge magnitude and timing relative to the position of the LCS. Plain Language Summary The Loop Current System (LCS) is a large, persistent, and dynamic flow that moves saline water in, around, and out of the GoM. On the Northern coast, freshwater is deposited by the Mississippi and Atchafalaya River System. When this freshwater encounters the LCS, it can change the distribution of surface salinity for the entire GoM. When there is less freshwater entering the GoM, the Loop Current (LC) can move Northward and likely deposit saltier water into the Mississippi River. In the opposite case, excessive amounts of freshwater can push the LC away from the coast and create a direct pathway out of the GoM. Through this research, we aim to determine the relative relationship between these mechanisms during flood and drought years. We find that river input is the most important in determining salinity patterns, followed by the amount of water coming into the Gulf from the south then lastly freshwater flux away from the river mouths. Additionally, we find that variability in the amount of freshwater input from 1 year to the next can affect the state of the LCS, further enhancing or dampening the expected salinity patterns. Key Points Salinity patterns driven by the interaction of the Loop Current (LC) and freshwater input during flood and drought years Quantification of the relative impacts of freshwater input variability input and LC circulation on basin‐wide salinity patterns Anomalous basin‐wide salinity patterns can be enhanced or dampened based on timing of LC evolution

In 2022, the Yangtze River Basin (YRB) experienced an unprecedented drought with long-term, large-scale, and severe consequences for agriculture, ecology, industrial production, and economic life. In order to investigate the evolution characteristics of the drought event in 2022, and discuss the similarities and differences with its similar historical drought years, this study focused on droughts during July–October (summer and autumn). The Standardized Precipitation Evapotranspiration Index was employed as a drought indicator. To analyze the spatial and temporal distribution patterns of drought and its mutability and periodicity in the YRB during 1951–2022, this study utilized the Empirical Orthogonal Function (EOF), Pettitt test, and wavelet analysis methods. We obtained similar drought years with the 2022 drought spatial pattern using the clustering method. The results show that the drought in the YRB in 2022 mainly presented a \"basin-wide\" drought spatial distribution pattern based on the first mode of EOF. The main periodicity of the \"basin-wide\" drought spatial distribution pattern was about 50 years. The July drought distribution patterns in 1952, 1953, and 2006 were most similar to that in 2022; however, the drought evolution patterns were obviously different after August. In comparison, the YRB experienced the largest drought-impacted area in 2022, and the impacted area proportions of severe and extreme drought increased at the fastest speed.

Recurrent hydrological droughts (streamflow deficits) are highly impactful and challenge water management. Regional studies have provided some evidence of drought-rich periods at specific time scales. However, it is yet unclear where and when droughts cluster in time. Here, we test for significant temporal hydrological drought clustering at subseasonal to multi-year time scales in different climate zones around the world using two different clustering metrics, i.e. the dispersion index and Ripley’s K . We find that (1) only 10% of the catchments show temporal hydrological drought clustering, (2) hydrological droughts cluster from seasonal to 3-year time scales with clustering being strongest at an annual time scale; (3) arid catchments with a low snow fraction are most prone to temporal drought clustering; and (4) temporal clustering is more pronounced for hydrological than for meteorological droughts. These results suggest that besides climatic drivers, land-surface processes importantly influence the temporal clustering behavior of hydrological droughts.

The trend analysis in the time series of crop production is an important tool to make future plans and to take the appropriate decisions for sustainability in food production and future food security. The objective of this study was to assess the trend analysis of the yield of five different major kinds of cereal (paddy, maize, millet, wheat, and barley) from 1985 to 2018 in six South Asian countries (Nepal, India, Pakistan, Bhutan, Afghanistan, and Bangladesh). The average annual yields of cereals in five quadrennial drought years (1985, 1989, 1993, 1997, and 2002) were estimated. The results revealed that the yield of major cereals had an increasing trend over the study period. The reasons for the fluctuations in the production were due to the changing climates, increasing global warming, the development of new hybrids and cultivars, the adoption of new practices by the farmers, economic constraints, and agronomic constraints. For improving the production of cereal crops, the use of modern technology should be increased, and the agricultural organizations should provide full support at the country level.

Multi-year droughts (MYDs) are extreme drought events leading to long-lasting impact. Due to their limited number in observational records, global climate models with large ensembles can contribute to understanding by increasing the sample size of MYDs. However, the knowledge on their ability to simulate MYDs is limited on a global scale. In this study, we evaluate six different CMIP6 models in simulating MYDs by comparing them to ERA5. We assess frequency, time spent in MYDs versus shorter droughts, seasonality, and physical drivers. The multi-model mean (MMM) performs robustly across these metrics, with strong inter-model agreement in deviations from ERA5. Deviations from ERA5 and inter-model spread are larger for MYD drivers compared to normal drought drivers. This can result from either model biases, ERA5 biases, or a limited sample size of MYDs within ERA5. The differences between the MMM and ERA5 are explained primarily by internal variability, which underscores the value of large ensembles for studying rare extremes such as MYDs.

Abstract Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901–2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3–4 years) and 4 to 5 consecutive drought years (once in 120 years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R2 (0.8–0.88) between observed and predicted data and low RMSE (2.6–2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R2 (0.71–0.75) and RMSE of 11.8–14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.

Accurately characterizing historical drought events is critical for understanding their spatial and temporal variability and for improving future drought projections. This study investigates extreme drought years across Europe using three complementary drought indicators: the Palmer drought severity index (PDSI, based on tree-ring width), the standardized precipitation evapotranspiration index (SPEI, based on stable oxygen isotopes in tree rings), and the soil moisture index (SMI, based on high-resolution climate modeling). We analyze the common period 1766–2018 simultaneously across all three reconstructions to enable direct cross-indicator comparisons, a scope not typical of prior single-indicator studies. When analyzing year-to-year variability, the driest European years differ by indicator (PDSI—1874, SPEI—2003, and SMI—1868). Quantitatively, the values exhibited are as follows: PDSI 1874 (M = −1.97; A = 64.4%), SPEI 2003 (M = −1.16; A = 90.1%), and SMI 1868 (M = 0.21; A = 83.4%). Multi-year extremes also diverge: while PDSI identifies 1941–1950 as the driest years (M = −0.82; A = 42.1%), SPEI highlights 2011–2018 (M = −0.36; A = 46.6%), and SMI points to 1781–1790 as the driest years, followed by 2011–2018. Trends in drought-covered areas show a significant European-scale increase for SMI (+0.52%/decade, p < 0.05) and regional increases for MED in SMI (~+1.1%/decade, p < 0.001) and for CEU in SPEI (+0.42%/decade, p < 0.05) and SMI (+0.6%/decade, p < 0.001). At the regional scale (Mediterranean—MED, central Europe—CEU, and northern Europe—NEU), the driest years/decades and spatial footprints vary by indicator, yet all the indicators consistently identify drought hotspots such as the MED. We also found that drought is significantly influenced by large-scale atmospheric drivers. A canonical correlation analysis (CCA) between summer geopotential height at 500 mb (Z500) and drought reconstructions indicates that drought-affected regions are, in general, associated with atmospheric blocking. The canonical series are significantly correlated at r = 0.82 (p < 0.001), with explained variances of 12.78% (PDSI), 8.41% (SPEI), and 14.58% (SMI). Overall, our study underscores the value of multi-indicator approaches: individual indicators provide distinct but complementary perspectives on European drought dynamics, improving the historical context for assessing future risk.

Soil moisture is a key parameter in the prediction of southwest monsoon rainfall, hydrological modelling, and many other environmental studies. The studies on relationship between the soil moisture and rainfall in the Indian subcontinent are very limited; hence, the present study focuses the association between rainfall and soil moisture during different monsoon seasons. The soil moisture data used for this study are the ESA (European Space Agency) merged product derived from four passive and two active microwave sensors spanning over the period 1979–2013. The rainfall data used are India Meteorological Department gridded daily data. Both of these data sets are having a spatial resolution of 0.25° latitude–longitude grid. The study revealed that the soil moisture is higher during the southwest monsoon period similar to rainfall and during the pre-monsoon period, the soil moisture is lower. The annual cycle of both the soil moisture and rainfall has the similitude of monomodal variation with a peak during the month of August. The interannual variability of soil moisture and rainfall shows that they are linearly related with each other, even though they are not matched exactly for individual years. The study of extremes also exhibits the surplus amount of soil moisture during wet monsoon years and also the regions of surplus soil moisture are well coherent with the areas of high rainfall.

Based on the daily temperature and precipitation data from 1961 to 2021 obtained from 2019 national meteorological stations of China, and by means of Meteorological Drought Comprehensive Index (MCI) and some improved identification methods, we identified all drought event processes in all the seven regions of China in this paper. We also carefully analyzed these regional droughts and made following conclusions: drought was spreading towards southern China with an increasing frequency; consecutive drought year group occurred in all the seven regions of China with an increasing frequency under the background of global warming; both drought duration and comprehensive intensity consistently changed with the area affected by drought, with a correlation coefficient of 0.52–0.67 and 0.88–0.99 respectively, and passed the significance test of 0.05. The method used in this paper could be employed to effectively monitor and evaluate drought processes from multiple dimensions including duration, comprehensive intensity and area affected by drought. Thus, it actually provided a helpful decision-making basis for governments to prevent the risk of drought.