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Precipitation whiplash, including abrupt shifts between wet and dry extremes, can cause large adverse impacts on human and natural systems. Here we quantify observed and projected changes in characteristics of sub-seasonal precipitation whiplash and investigate the role of individual anthropogenic influences on these changes. Results show that the occurrence frequency of global precipitation whiplash is projected to be 2.56 ± 0.16 times higher than in 1979–2019 by the end of the 21 st Century, with increasingly rapid and intense transitions between two extremes. The most dramatic increases of whiplash show in the polar and monsoon regions. Changes in precipitation whiplash show a much higher percentage change than precipitation totals. In historical simulations, anthropogenic greenhouse gas (GHG) and aerosol emissions have increased and decreased precipitation whiplash occurrences, respectively. By 2079, anthropogenic GHGs are projected to increase 55 ± 4% of the occurrences risk of precipitation whiplash, which is driven by shifts in circulation patterns conducive to precipitation extremes. This study shows that the occurrence frequency of global precipitation whiplash is projected to be ~2.6 times higher by the end of the 21st century compared to 1979–2019, with increasingly rapid and intense transitions between the two extremes.

Climate change exerts great influence on streamflow by changing precipitation, temperature, snowpack and potential evapotranspiration (PET), while human activities in a watershed can directly alter the runoff production and indirectly through affecting climatic variables. However, to separate contribution of anthropogenic and natural drivers to observed changes in streamflow is non-trivial. Here we estimated the direct influence of human activities and climate change effect to changes of the mean annual streamflow (MAS) of 96 Canadian watersheds based on the elasticity of streamflow in relation to precipitation, PET and human impacts such as land use and cover change. Elasticities of streamflow for each watershed are analytically derived using the Budyko Framework. We found that climate change generally caused an increase in MAS, while human impacts generally a decrease in MAS and such impact tends to become more severe with time, even though there are exceptions. Higher proportions of human contribution, compared to that of climate change contribution, resulted in generally decreased streamflow of Canada observed in recent decades. Furthermore, if without contributions from retreating glaciers to streamflow, human impact would have resulted in a more severe decrease in Canadian streamflow.

Understanding the variability of spatial extents of precipitation extremes favors an accurate assessment of the severity of disasters caused by extreme precipitation events. Using a restricted neighborhood method, we identify the spatial extents of global precipitation extremes over 1983–2018 and examine their spatiotemporal variability and associated changes. Results show that the mid-latitudes shows the largest spatial extent of precipitation extremes, and the spatial extents in non-tropical regions over the Northern Hemisphere show significant seasonal differences. In non-monsoon regions, the spatial extents of precipitation extremes in autumn and winter are larger than those in spring and summer, and the annual average spatial extents of precipitation extremes all exceed 500 km, which are larger than those in monsoon regions. All the five non-monsoon regions over the Northern Hemisphere and three monsoon regions in the western Pacific show statistically significant increases in the spatial extent of precipitation extremes in most seasons.

Changes in precipitation seasonality or redistribution of precipitation could exert significant influences on regional water resources availability and the well-being of the ecosystem. However, due to the nonuniform distribution of precipitation stations and intermittency of precipitation, precise detection of changes in precipitation seasonality on the global scale is absent. This study identifies and inter-compares trends in precipitation seasonality within seven precipitation datasets during the past three decades, including two gauge-based datasets derived from the Climatic Research Unit (CRU) and the Global Precipitation Climatology Centre (GPCC), one remote sensing-retrieval obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), three reanalysis datasets obtained from National Centers for Environmental Prediction reanalysis II (NCEP2), European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), and Modern Era Reanalysis for Research and Applications Version 2 (MERRA2), and one precipitation dataset merged from above three types, Multi-Source Weighted Ensemble Precipitation Version 1.2 (MSWEP_V1.2). Values of two indices representing the precipitation seasonality, the normal seasonality index (SI) and the dimensionless seasonality index (DSI), are estimated for each land grid in each precipitation dataset. The results show that DSI is more sensitive to changes in the temporal distribution of precipitation as it considers both annual amount and monthly fluctuations of precipitation, compared to SI that only considers monthly fluctuations of precipitation. There are large differences in precipitation seasonality at annual and climatologic scales between precipitation datasets for both SI and DSI. Within the seven precipitation datasets, PERSIANN-CDR SI and DSI show high precipitation seasonality while CRU SI, and ERA-Interim and MERRA2 DSI show the low precipitation seasonality in all continental regions. During 1988–2013, PERSIANN-CDR, NCEP2 and ERA-Interim show more widespread, statistically significant trends in precipitation seasonality than other four precipitation datasets. PERSIANN-CDR and NCEP2 show statistically significant decreases in SI over Middle East and Central Asia, while ERA-Interim, MERRA2 and MSWEP_V1.2 SI increase over Central and South Africa. Increases in SI over the most of South America are significant. Regions of Canada/Greenland/Iceland, East and South Africa show significant increases in precipitation seasonality, while South Europe/Mediterranean and Central Africa show significant decreases in precipitation seasonality in most datasets. Although time series of seasonality indices values fluctuate correlatively in recent three decades, there are no regions on which all precipitation datasets show a consistent, statistically significant, positive or negative trend in indices of precipitation seasonality. These inconsistent changes in precipitation seasonality within various precipitation datasets imply the importance of choosing dataset when studying changes in regional precipitation seasonality.

Five reanalysis datasets—National Centers for Environmental Prediction reanalysis II (NCEP-2), NCEP Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), Japanese 55-year Reanalysis Project (JRA-55), and National Aeronautics and Space Administration (NASA) Modern Era Reanalysis for Research and Applications Version-2 (MERRA-2)—are selected to estimate meteorological droughts of China using three drought indices—the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). Drought indices, drought areas and drought severity estimated for China from these reanalysis datasets are assessed against corresponding results obtained from observed climate dataset of China using Nash–Sutcliffe efficiency (NSE), correlation coefficient, and the analysis of time series. Further, temperature, precipitation and potential evapotranspiration data of the five reanalysis datasets are also compared against the observed dataset. Drought indices and drought areas estimated from reanalysis datasets are generally more representative of historical droughts that had occurred in eastern China than in western China. However, the performance of these five reanalysis datasets in representing the drought severity is unsatisfactory in both western China and eastern China. SPEI is generally more representative than PDSI and SPI partly because temperature and potential evapotranspiration data of reanalysis datasets are generally better than precipitation data. PDSI is also based on a supply-and-demand model of soil moisture but estimating the demand of soil moisture is complicated. Therefore, SPEI is preferred over PDSI and SPI as the drought index to characterize the meteorological droughts of China. Climate data and meteorological drought characteristics of eastern China are best represented by JRA-55, while that of western China are best represented by MERRA-2. From 1980 to 2014, statistically significant increasing trends in annual drought areas and drought severity are detected from JRA-55 and observed climate datasets in eastern China, but they are only detected from observed dataset in western China.

The evaluation of offshore wind energy resources is important to the construction of offshore wind power facilities. In this paper, using four models from CMIP6 and the ERA5 reanalysis dataset, a deep learning model termed SwinWind was developed and proposed to evaluate future offshore wind energy resources in Southeastern China for the periods 2020–2050 and 2070–2100. The feature extraction capability of the Swin Transformer was utilized to construct a bias correction and downscaling framework. This approach achieves performance comparable to existing high-cost models while significantly reducing computational costs and complexity. The SwinWind model corrected most of the biases and effectively learned spatial relationships, successfully performing the downscaling task. Based on future wind speed projections derived from the SwinWind model, this study presents a comprehensive evaluation of offshore wind resources, examining five critical dimensions: resource abundance, efficiency, stability, the impact of extreme winds, and economic feasibility. It is projected that offshore wind resources around Shanghai, Jiangsu and Zhejiang will experience a decline in the 21st century, while offshore wind resources around the Guangdong, Fujian and the Beibu Gulf show an increasing trend. The evaluation index shows that the coastal areas of Guangdong and the southern coastline of Taiwan are the most suitable locations for wind power exploitation. The Taiwan Strait, which has the highest wind energy density, is not the best spot due to its extreme wind speed and unstable wind resources. This study provides an important reference for the location of wind farms with practical application value.

Under the possible impact of climate warming, recent changes in dry and wet spells have contributed significantly to climate-related hazards around the world. In this work, spatial and temporal variations in dry and wet spells over Canada are investigated using daily precipitation data from 1979 to 2018. The time-varying relationships between precipitation spells and large-scale climate anomalies are modeled using a nonstationary generalized extreme value (GEV) distribution and Bayesian quantile regression. Over the period 1979–2018, significant changes in dry and wet spells have been observed across Canada, particularly in the southern Canadian Prairies (CP), where both the number and duration of dry spells show positive trends. Dry and wet spells over many parts of Canada are nonstationary under the effects of the El Niño–Southern Oscillation (ENSO) and the Pacific–North American pattern (PNA), with PNA having stronger effects on annual maximum dry spells than ENSO, especially in the central CP and eastern Ontario. For western Canada, the influence of ENSO on dry spells tends to be relatively strong, especially for dry spells of high quantiles, as El Niño generally induces atmospheric moisture deficit. For central Canada, ENSO and PNA have a negative (positive) impact on the wet spell duration of low (high) quantiles. For eastern Canada, PNA is negatively correlated with the duration of wet spells, especially for wet spells of high quantiles. Therefore, a better understanding of the spatial and temporal variability in dry and wet spell return periods will be useful for the effective management of water resources, and for developing effective disaster mitigation measures against the possible social and economic impacts of climate-related hazards.

This study inter-compares the spatiotemporal features of precipitation extremes at global and regional scales within six daily precipitation datasets, i.e., gauge-based (Global Precipitation Climatology Center dataset, GPCC), satellite-retrieval (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR), three reanalysis datasets (ERA-Interim, ERAI; the National Center for Atmospheric Research Reanalysis 2, NCEP2; and the WATCH-Forcing-Data-ERA-Interim, WFDEI), and products merged from the three type datasets (the Multi-Source Weighted-Ensemble Precipitation, MSWEP). All datasets reproduce similar spatial patterns of both annual and seasonal precipitation extremes over the period from 1979 to 2017. Compared to the reference dataset gauge-based GPCC, the reanalysis WFDEI outperforms among six products with spatial correlation coefficients of 0.89 and 0.80 for the annual extreme indices (i.e., annual total amount of 95th precipitation and maximum 1-day precipitation), respectively. The satellite-based product PERSIANN-CDR performs better than reanalyses and merged datasets in capturing the temporal variability of the intensity and amount of precipitation extremes with similar changing tendencies and magnitudes of about 45 mm day −1 and 230 mm at the global scale, respectively. The reanalyses and merged products underestimate the intensity of precipitation extremes. The selected six datasets behave differently in various regions. For the percentile-based frequency of precipitation extremes, NCEP2 performs well in regions of the Southeast Asian (SEA) and Amazon (AMZ), while WFDEI better matches GPCC over East North America (ENA) and North Australia (NAU) in both spatial patterns and temporal changes with correlation coefficients of 0.84 and 0.90, respectively. For the intensity features of annual precipitation extremes, NCEP2 performs better than other four datasets over regions of SEA, AMZ and West Africa (WAF). ERAI and WFDEI are consistent with GPCC in ENA and NAU with correlations coefficients of the intensity between ERAI (WFDEI) and GPCC are 0.82 (0.77) and 0.78 (0.64) for ENA and NAU, respectively. For the intensity of seasonal precipitation extremes, GPCC shows the highest estimates in regions of SEA, AMZ, ENA and WAF. ERAI and WFDEI perform better in reproducing the spatial patterns of seasonal precipitation extremes in all regions. NCEP2 (ERAI and WFDEI) show(s) consistent temporal variability of seasonal precipitation extremes with GPCC in regions of AMZ and WAF (ENA and NAU). Overall, there are large discrepancies in the absolute values of daily precipitation among datasets, and performances of non-gauged-based precipitation datasets in capturing the spatiotemporal variability of precipitation extremes are dependent on seasons, regions, and time periods.

Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale.

Alternate wetting and drying (AWD) irrigation is widely adopted to save water in rice production. AWD practice shifts lowland paddy fields from being continuously anaerobic to being alternately anaerobic and aerobic, thus affecting nitrogen (N) transformations in paddy field soils. Using the barometric process separation technique, a large number of soil cores sampled from lowland paddy field soil profiles were measured for gross nitrification and denitrification rates under different temperature and soil moisture conditions. The gross nitrification and denitrification rates vary with rice growth stages and range between 1.18–30.8 and 0.65–13.54 mg N m−3 h−1, respectively. Results indicate that both gross nitrification and denitrification rates increased with the increase in temperature in all three studied soil layers. Gross nitrification rates significantly decrease with increasing soil moisture while denitrification rates increase, and different soil layers demonstrated different rates of variation to the increase in soil moisture. Gross nitrification rates in the cultivated horizon layer decreased more sharply with the increase in soil moisture. High soil water content is favorable to denitrification of all soil layers.