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Abstract Extreme precipitation can lead to major flooding, impacting human health and safety. Thus, reliable projections of population and GDP exposure to future extreme precipitation are imperative. Here, we quantify future precipitation characteristics from robust emergent constraint relationships between historical and future monthly precipitation extremes (99th percentile) across 19 CMIP6 models (r2 > 0.7 in 74–84% of 0.5° global land grids), and narrow uncertainty by 37.0–39.5% (absolute reduction being 0.753–0.774 mm/day). The constrained grid‐averaged future 99th percentile extreme is 6.96 ± 0.0059, 7.03 ± 0.0061, 7.11 ± 0.0063, and 7.29 ± 0.0067 mm/day, under SSP126, SSP245, SSP370, and SSP585, respectively, which exceeds historical extremes substantially in terms of intensity (12.9–19.7%) and frequency (1.6–2.3 times more). Future population and GDP exposed to 99th percentile extreme precipitation grow quickly, and are projected to exceed 1 million people in 27–40 countries and 10 billion USD (2005 Purchasing‐Power Parity) in 48–77 countries. Growth of future population exposure is dominated by an increase in extreme precipitation frequency rather than a rise in population, especially in developed countries. GDP exposure is controlled by the coupled effects of rapid socio‐economic development and significant shifts in precipitation frequency. Using indices of socio‐economic vulnerability, government effectiveness and economic freedom, we identify the unequal situation that high‐risk countries with high exposure are commonly characterized by low GDP per capita and high sociopolitical instability.

Abstract The quasi‐global availability of satellite‐based precipitation products (SPPs) holds significant potential for improving hydrological modeling skill. However, limited knowledge exists concerning the impacts of different SPP error type on hydrological modeling skill and their sensitivity across different climate zones. In this study, forcing data sets from 10 SPPs were collected to drive hydrological models during the period 2001–2018 for 366 catchments across China. Here, we analyze the impact of the SPP errors associated with different precipitation intensities (light, moderate, and heavy) and different precipitation signatures (magnitude, variance, and occurrence) on the performance of hydrological simulations, and rank the sensitivities of SPPs errors for four major Köppen‐Geiger climate zones. The results show that heavy precipitation in SPPs is generally associated with higher errors than light and moderate precipitation when compared to gauge‐based precipitation observations, but hydrological model skill is more sensitive to errors from moderate precipitation than from heavy precipitation. The probability of moderate precipitation detection was identified as the most sensitive metric in determining hydrological model performance, with sensitivities of 0.58, 0.39, 0.59, and 0.47 in the temperate, boreal, arid, and highland climate zones, respectively. The variance error and magnitude error for heavy precipitation from SPPs were also identified as sensitive factors for hydrological modeling in the temperate and arid climate zones, respectively. These findings are crucial for enhancing the understanding of interactions between SPPs uncertainty and hydrological simulations, leading to improved data accuracy of precipitation forcing and the identification of appropriate SPPs for hydrological simulation in China.

Dry and humid-heat extremes are two types of heat extremes, each exhibiting unique climatological characteristics and impacts on different sectors of society. Using historical simulations and projections produced under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) by models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show a comparative assessment of the future changes in dry- and humid-heat extremes over global land. Relative to 1995–2014, projections for the mid-term future (2041–2060) and long-term future (2081–2100) periods suggest that most global regions will experience an increase in frequency and intensity of both dry- and humid-heat extremes, especially the tropical regions. In these future periods, the peak occurrences of dry- and humid-heat extremes in mid-to high-latitude regions often occur within the same month. However, there will be a one-to two-month gap between the peak occurrences of dry- and humid-heat extremes in tropical regions, primarily due to monsoonal circulations that introduce variability by causing dry-heat extremes before the onset of monsoons and humid-heat extremes as the monsoons commence. This suggests the need for sector-specific adaptation strategies during different periods of the year for tropical regions. Under both future scenarios, whether considering individual exposure or land area, the average level of exposure to extreme humid-heat days is projected to increase more significantly compared to dry-heat days. The above results highlight the risks associated with the intensification of humid heat in future climate scenarios and warrant the development of effective strategies to mitigate the adverse effects.

Abstract Planting has been widely adopted to battle the loss of salt marshes and to establish living shorelines. However, the drivers of success in salt marsh planting and their ecological effects are poorly understood at the global scale. Here, we assemble a global database, encompassing 22,074 observations reported in 210 studies, to examine the drivers and impacts of salt marsh planting. We show that, on average, 53% of plantings survived globally, and plant survival and growth can be enhanced by careful design of sites, species selection, and novel planted technologies. Planting enhances shoreline protection, primary productivity, soil carbon storage, biodiversity conservation and fishery production (effect sizes = 0.61, 1.55, 0.21, 0.10 and 1.01, respectively), compared with degraded wetlands. However, the ecosystem services of planted marshes, except for shoreline protection, have not yet fully recovered compared with natural wetlands (effect size = −0.25, 95% CI −0.29, −0.22). Fortunately, the levels of most ecological functions related to climate change mitigation and biodiversity increase with plantation age when compared with natural wetlands, and achieve equivalence to natural wetlands after 5–25 years. Overall, our results suggest that salt marsh planting could be used as a strategy to enhance shoreline protection, biodiversity conservation and carbon sequestration.

Abstract Peer‐review is the foundation and the safeguard of scientific research. Without the dedication of our reviewers, the journal would not have been successful. In 2023, 269 reviewers completed 434 reviews for the 174 manuscripts submitted to GeoHealth. Our reviewers are from all continents except Antarctica. Besides reviewers from North America, China, Europe, and China, we started to have reviewers from India, Latin America, and Africa. GeoHealth editorial board is committed to expanding the readership, authorship, and reviewership to other countries. If you have already reviewed for us, no matter where or who you are, we hope you and your colleagues will consider GeoHealth a home for your work. Below is the list of reviewers who completed more than two reviews or have outstanding quality reviews. Two of our reviewers are being nominated for AGU best reviewers awards.

Abstract Fire weather compounded by extremely hot and dry conditions often severely impacts society and ecosystems. To mitigate and better adapt to these compound fire weather (CFW) events, a better understanding of recent and future CFW trends is needed. Here we show that in the period 1981–2020, the global average frequency and intensity of CFW events increased by 0.6 days/yr and 0.4%/yr, respectively. Increases in temperature and decreases in relative humidity were responsible for significant trends in the frequency of CFW events in 81.7% and 58.6% of locations, respectively. The same trends contributed to significant increases in CFW intensity in 72.1% and 57.9% of locations. We further demonstrate that anthropogenic climate change (due primarily to greenhouse gas emissions) has aggravated the frequency and intensity of CFW events, particularly in the Amazon region, with over 2‐fold and 1.3‐fold increases, respectively. Future projections reveal that other (individual) fire weather events are likely to shift toward CFW events accompanied by hot‐dry conditions, along with an expected rise in CFW intensity. Furthermore, the increased occurrence of CFW events is likely to substantially augment future population exposure to CFW conditions. Under the SSP5‐8.5 scenario, climate change is estimated to contribute 62.6% of the projected increase in population exposure to CFW events by the end of this century. Our findings underscore the urgent need for strong climate action to reduce population exposure to the growing threat of future fire weather events compounded with hot and dry conditions.

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk.In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

Key Points The editors thank the 2022 peer reviewers In 2022, GeoHealth benefited from 333 reviews provided by 245 of our peers A number of individuals submitted multiple reviews for GeoHealth in 2022

Understanding the time required for meteorological drought to propagate to hydrological drought is crucial for producing early warnings of future hydrological droughts. However, most previous studies of this topic have used observed runoff (or streamflow), which usually has been disturbed by human activities, and accordingly, the calculated drought propagation time (DPT) cannot accurately characterize the real propagation characteristics under natural conditions. In this study, we quantified the meteorological to hydrological DPT during the period 1962–2018 based on natural runoff and streamflow datasets and then analyzed the primary meteorological factors in influencing the spatial distribution of DPT. The results show the following: (1) The overall average DPT in China is about 6 months, decreasing from the northwest (9–12 months) to the southeast (1–2 months), and the DPT in spring and winter is generally longer than in summer and autumn. (2) The most sensitive areas for drought propagation during the period 1991–2018 increased in extent by 1.73% when compared with the extent during the period 1962–1990, and river-flow routing processes led to longer DPTs in southeast China and shorter DPTs in northwest China. (3) Precipitation and maximum temperature are the dominant meteorological factors influencing the spatial variability of DPT across China, while river-flow routing changes one of these dominant factors from maximum temperature to mean temperature.

The “Grain for Green” project (GGP) was launched in 1999 on China’s Loess Plateau to reduce soil erosion, which had far-reaching impacts on the local eco-hydrological processes. In this study, we use monthly runoff depth and precipitation datasets spanning 1961 to 2015 for 16 primary basins of the plateau to reveal changes in runoff generation capacity before and after the GGP. We use a Budyko-based elasticity method to calculate the runoff depth (R1) and runoff coefficient (C1) exclusively attributable to land use/cover change. Results indicate that the mean annual runoff coefficients (C0) decline by 26%–76% from the periods 1961–1999 to 2000–2015. The annual observed runoff depth (R0) and C0 for 75% of basins show significant downward trends during 1961–1999; after the implementation of the GGP, both annual R0 and annual C0 for over 50% of basins show upward trends. The study further finds that the increase of erosive rainfall during the period 2000–2015, whose mean increasing rate reaches 4.6 mm/year, is the main reason for the upward trends of R0 and C0. After removing the effect of precipitation variation during this period, we find that 11 out of 16 basins show decreased trends for C1, with the downward rate between 2.4% and 6.0% per year. The reduction rate in semi-arid areas is about four times the rate in semi-humid areas. The results remind us to consider the carrying capacity of local water resources when implementing soil and water conservation measures across the Loess Plateau.

The water discharge and sediment load have been increasingly altered by climate change and human activities in recent decades. For the Pearl River, however, long-term variations in the sediment regime, especially in the last decade, remain poorly known. Here we updated knowledge of the temporal trends in the sediment regime of the Pearl River at annual, seasonal and monthly time scales from the 1950s to 2020. Results show that the annual sediment load and suspended sediment concentration (SSC) exhibited drastically decreased, regardless of water discharge. Compared with previous studies, we also found that sediment load and SSC reached a conspicuous peak in the 1980s, and showed a significant decline starting in the 2000s and 1990s, respectively. In the last decade, however, water discharge and sediment load showed slightly increasing trends. At the seasonal scale, the wet-season water discharge displays a decreasing trend, while the dry-season water discharge is increasing. At the monthly scale, the flood seasons in the North and East Rivers typically occur one month earlier than that in the West River due to the different precipitation regimes. Precipitation was responsible for the long-term change of discharge, while human activities (e.g. dam construction and land use change) exerted different effects on the variations in sediment load among different periods. Changes in the sediment regime have exerted substantial influences on downstream channel morphology and saltwater intrusion in the Greater Bay Area. Our study proposes a watershed-based solution, and provides scientific guidelines for the sustainable development of the Greater Bay Area.

Abstract Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures.

Abstract Streamflow sensitivity to climate change is an important indicator for evaluating the effects of climate change on terrestrial water. This study analyzed the spatial pattern and seasonality of streamflow sensitivity to climate change for 425 catchments across China. The results indicate that precipitation is consistently a more important contributor than temperature to streamflow variability (mean streamflow sensitivity to annual precipitation [εP,a] = 0.92; absolute mean sensitivity to annual temperature [εT,a] = 0.05). Meanwhile, the seasonal sensitivity evaluation found that streamflow response to precipitation change in warm seasons (mean εP,w = 0.90) is significantly greater than in cool seasons (mean εP,c = 0.46), but the sensitivity of streamflow to changes in temperature was stronger in cool seasons than in warm seasons. The magnitude of streamflow sensitivity to temperature in cool seasons is significantly affected by elevation (Pearson's r = 0.31, P

Study region: The Tibetan Plateau, known as the “Water Tower of Asia”, is the source of many rivers in Asia, and its water conservation function is the basis for the optimization of the ecological barrier and the water security of Asian countries. Yet, the Tibetan Plateau is also an ecologically fragile area, facing numerous ecological issues and climate change risks, so the response of the water conservation function to the changing environment has received much attention. Study focus: This study systematically analyzed the variation trend of the water conservation function during 1961–2017, using the InVEST model simulations, linear regression analysis, and coefficients of variation. New hydrological insights for the region: The results show that the average annual water conservation of the Tibetan Plateau is about 256 billion m3, showing a decreasing trend from southeast to northwest, and the important areas scatter in the southeastern of the plateau, such as the lower Yarlung Zangbo watershed, the Nujiang watershed, and the Yalong watershed. The water conservation volume of the Tibetan Plateau was on the rise (P

Rainfall erosivity, one of the factors in the Universal Soil Loss Equation, quantifies the effect of rainfall and runoff on soil erosion. High-resolution data are required to compute rainfall erosivity, but are not widely available in many parts of the world. As the temporal resolution of rainfall measurement decreases, computed rainfall erosivity decreases. The objective of the paper is to derive a series of conversion factors as a function of the time interval to compute rainfall erosivity so that the R factor computed using data at different time intervals could be converted to that computed using 1-min data. Rainfall data at 1-min intervals from 62 stations over China were collected to first compute the ‘true’ R factor values. Underestimation of the R factor was systematically evaluated using data aggregated at 5, 6, 10, 15, 20, 30, and 60-min to develop conversion factors for the R factor and the 1-in-10-year storm EI30 values. Compared with true values, the relative error in R factor using data at fixed intervals of ≤10min was 10% using 15-min data for 66.1% of stations and >20% using 30-min data for 61.3% of stations. The conversion factors for the R factor, ranging from 1.051 to 1.871 for 5 to 60-min data, are higher than those for the 1-in-10-years storm EI30, ranging from 1.034 to 1.489 for the 62 stations.

Key Points The editors thank the 2021 peer reviewers In 2021, GeoHealth benefited from 365 reviews provided by 241 of our peers A number of individuals submitted multiple reviews for GeoHealth in 2021

Abstract Documenting the spatiotemporal changes in vegetation cover and hydrological cycle of the Earth system and understanding how they interact are important especially under climate warming. In this research, we quantified the changes in vegetation and evapotranspiration (E) across China during 1982–2015 and then revealed the complex relationships in climate–vegetation–evapotranspiration system. Results show that the upward trend in vegetation leaf area index (LAI) during 2000–2015 (an increase of 0.95% per year) was almost 8 times the trend during 1982–1999. The zones of the Loess Plateau and the Three‐North Shelter Forest Program are the most notable areas for LAI increases between these two periods, with increases of 11.65% and 2.87%, respectively. Increased LAI, along with the warming climate, has accelerated E across China in the past several decades, and the annual increase in the E rate was 0.34% (1.34 mm year−1) during 1982–1999 and 0.40% (1.62 mm year−1) during 2000–2015. The zones of the Loess Plateau and the karst landform are the most notable areas for transpiration increases, with individual increases of 10% and 5%, respectively. In general, the dominant causes for evaporation changes across all of China are temperature and precipitation, while the main reasons for transpiration changes include temperature, LAI, and sunshine duration. This study improves our understanding of the relationships within the climate–vegetation–evapotranspiration system and provides important support for future ecological policies across China.

The distribution of alpine vegetation is highly sensitive to climate change, which attracted the attention of climate scientists as well as ecologists. However, the dominant factors of vegetation distribution showed greatly spatiotemporal variation, especially the alpine grasslands on the Tibetan Plateau, which is known as an amplifier of climate warming. In this study, to identify the dominant factors of vegetation distribution, we verified the reliability and accuracy of the classification of alpine steppe and alpine meadow by Random Forest and tried to provide a new reference for the classification schemes. Dataset collected from the field investigations (200 sites) and previous publications(200 sites) were used in this study for model calibration and validation. Then climate models of CMIP6 were used to forecast the underlying transfers and changes of vegetation boundary in the future. Our results revealed that precipitation may be the dominant driver in the alpine grassland distribution, with the highest relative importance of 41.26%. Through different climate scenarios from 2000 to 2100, the vegetation boundary showed a shift from northeast to southwest. Within the SSP5-8.5 scenario it moves from 94.50° to 93.49° and shifted from north to south by 0.34°. Our results demonstrated the significant role of precipitation in the alpine grassland distribution and revealed the migration of the alpine grassland ecosystem under different climate change scenarios. Our work may be useful to deal with global climate change, early warning, and grassland protection, as well as adaptive management of grassland.

The effects of heat stress are spatially heterogeneous owing to local variations in climate response, population density, and social conditions. Using global climate and impact models from the Inter-Sectoral Impact Model Intercomparison Project, our analysis shows that the frequency and intensity of heat events increase, especially in tropical regions (geographic perspective) and developing countries (national perspective), even with global warming held to the 1.5 °C target. An additional 0.5 °C increase to the 2 °C warming target leads to >15% of global land area becoming exposed to levels of heat stress that affect human health; almost all countries in Europe will be subject to increased fire danger, with the duration of the fire season lasting 3.3 days longer; 106 countries are projected to experience an increase in the wheat production-damage index. Globally, about 38%, 50%, 46%, 36%, and 48% of the increases in exposure to health threats, wildfire, crop heat stress for soybeans, wheat, and maize could be avoided by constraining global warming to 1.5 °C rather than 2 °C. With high emissions, these impacts will continue to intensify over time, extending to almost all countries by the end of the 21st century: >95% of countries will face exposure to health-related heat stress, with India and Brazil ranked highest for integrated heat-stress exposure. The magnitude of the changes in fire season length and wildfire frequency are projected to increase substantially over 74% global land, with particularly strong effects in the United States, Canada, Brazil, China, Australia, and Russia. Our study should help facilitate climate policies that account for international variations in the heat-related threats posed by climate change. Keywords: Global warming, Exposure, Heat-related extremes, 1.5 °C warming target

Abstract This study examined the overall performance of the climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the key energy and water fluxes over land. For this purpose, this study selected multiple land flux products as reference data sets and assessed the global spatial means, patterns, trends, seasonal cycles, and regional mean estimates of the sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) simulated by 32 CMIP6 models in recent decades. The global (Antarctica, Greenland, and hot deserts are not included) mean SH, LH, RN, RF, and PR simulated by the CMIP6 models are 37.55 ± 4.81 W m−2, 49.88 ± 5.31 W m−2, 89.10 ± 4.45 W m−2, 351.31 ± 95.28 mm yr−1, and 948.35 ± 88.77 mm yr−1, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) can provide robust estimates of global and regional land fluxes, which are within the ranges given by the reference data sets, and highly consistent spatiotemporal patterns of these fluxes. The comparison of CMIP6‐MED with the first preferred reference data sets shows that CMIP6‐MED generally overestimates the water and energy fluxes over land, except for the simulated RF and PR in the Amazon region. The most disagreements between CMIP6‐MED and the reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau. Finally, the sources of model biases are discussed. It is suggested that current land flux products should be widely used to optimize the structures and parameters of climate models in future work.

Abstract Identifying climate change hotspot regions is critical for planning effective mitigation and adaptation activities. We use standard Euclidean distance (SED) to calculate integrated changes in precipitation and temperature means, interannual variability, and extremes between different future warming levels and a baseline period (1995–2014) using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble. We find consistent hotspots in the Amazon, central and western Africa, Indonesia and the Tibetan Plateau at warming levels of 1.5°C, 2°C, and 3°C for all scenarios explored; the Arctic, Central America and southern Africa emerge as hotspots at 4°C warming and at the end of the 21st century under two Shared Socioeconomic Pathways scenarios, SSP3‐7.0 and SSP5‐8.5. CMIP6 models show higher SED values than CMIP5, suggesting stronger aggregated effects of climate change under the new scenarios. Hotspot time of emergence (TOE) is further investigated; TOE is defined as the year when the climate change signal first exceeds the noise of natural variability in 21st century projections. The results indicate that TOEs for warming would occur over all primary hotspots, with the earliest occurring in the Arctic and Indonesia. For precipitation, TOEs occur before 2100 in the Arctic, the Tibetan Plateau and Central America. Results using a geographical detector model show that patterns of SED are shaped by extreme hot and dry occurrences at low‐to‐medium warming, while precipitation and temperature means and extreme precipitation occurrences are the dominant influences under the high emission scenario and at high warming levels.

Abstract Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau.

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The Songhua River Basin is one of the main grain-producing areas in China, and local climate change has had a direct impact on grain production during the past several decades. This study focused on changes in mean and extreme climate conditions in the Songhua River Basin during the period 1961–2014. We founded that the precipitation decreased slightly over most of the basin; however, an increasing occurrence of extreme events was detected in the northern and eastern parts of the basin, especially for the number of very wet days and, correspondingly, how much those days contributed to total precipitation. By contrast, the mean, maximum and minimum temperature values between 1961 and 2014 showed statistically significant positive trends in almost all regions of the basin. The rate of change was greater for minimum temperature than maximum temperature, which resulted in a significant increasing trend for the number of warm nights and an overall decreasing trend in the annual mean diurnal temperature range. Generally, the magnitudes of changes in temperature were more remarkable than the magnitudes of changes in precipitation over the Songhua River Basin. If we take both precipitation and temperature changes into account, the areas sensitive to climate change during the last five decades were in the southern and central parts of the Songhua River Basin.

Key Points The editors thank the 2019 peer reviewers

Abstract The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(

How maize yield response to precipitation varies across a large spatial scale is unclear compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. This study provides a spatial-explicit quantification of maize yield response to precipitation in the contiguous United States and investigates how precipitation response is altered by natural and human factors using statistical and crop model data. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39%), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier, cooler, and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ∼90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change.

Frequent temperature extremes due to climate change have had serious effects on human society and the natural environment. Using a 0.25° × 0.25° gridded Tmax (daily maximum temperature) and Tmin (daily minimum temperature) data set and 12 global climate models simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we investigated variations in yearly temperature extremes in China during the past five decades with respect to four characteristics, namely, their start date, end date, frequency, and intensity. Results showed that the occurrence of nighttime extremes (the cold nights and warm nights) responded strongly to climate change. For 1961–2017, cold extremes started later (3.25 d/decade) and ended earlier (−4.58 d/decade), with decreased frequency (−6.56 d/decade), especially for cold nights, and weakened intensity (0.14 °C/decade). In the same period, warm extremes started earlier (−3.43 d/decade) and ended later (3.15 d/decade) with increased frequency (6.79 d/decade), especially for warm nights, and enhanced intensity (0.09 °C/decade). The spatial pattern of the variations was complex with anomalous regions. Multimodel ensembles (MMEs) from CMIP6 agreed well with observations regarding the average trends of temperature extremes over China, although detailed changes in spatial pattern were not captured adequately. The hazards of temperature extremes deserve close attention in the future due to the complex changes likely to occur across China for various characteristics of these temperature extremes under conditions of climate change.

A comprehensive 30×30 arc‐second resolution gridded soil characteristics data set of China has been developed for use in the land surface modeling. It includes physical and chemical attributes of soils derived from 8979 soil profiles and the Soil Map of China (1:1,000,000). We used the polygon linkage method to derive the spatial distribution of soil properties. The profile attribute database and soil map are linked under the framework of the Genetic Soil Classification of China which avoids uncertainty in taxon referencing. Quality control information (i.e., sample size, soil classification level, linkage level, search radius and texture) is included to provide “confidence” information for the derived soil parameters. The data set includes 28 attributes for 8 vertical layers at the spatial resolution of 30×30 arc‐seconds. Based on this data set, the estimated storage of soil organic carbon in the upper 1 m of soil is 72.5 Pg, total N is 6.6 Pg, total P is 4.5 Pg, total K is 169.9 Pg, alkali‐hydrolysable N is 0.55 Pg, available P is 0.03 Pg, and available K is 0.61 Pg. These estimates are reasonable compared with previous studies. The distributions of soil properties are consistent with common knowledge of Chinese soil scientists and the spatial variations over large areas are well represented. The data set can be incorporated into land models to better represent the role of soils in hydrological and biogeochemical cycles in China.

This study focuses on changing trends in precipitation across mainland China during the period 1957–2014. We explore the influence of the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and related large-scale atmospheric circulation variables on the changes in precipitation. The number of wet days showed statistically significant downward trends in North China, Jianghuai, South China, and Southwest of China, but upward trends on the Tibetan Plateau and in Northwest China. However, the number of very wet days increased in Jianghuai, South China and regions in Southwest China, and there was an increase in the spatial variability of a number of rainfall extremes over China. Because the changes in the frequency of wet days and very wet days were non-uniform, an increasing percentage of the total annual precipitation was derived from extreme events over large regions of mainland China. The ENSO and the PDO had a zonal influence on precipitation variability through the modulation of large-scale atmospheric circulation. Both the number of wet days and the frequency of extreme precipitation increased in southern Jianghuai and South China in El Niño years compared with La Niña years. A decrease (increase) in the number of wet days was observed in northern China (southeastern China) during positive PDO-phase years, which was likely a response to the large decrease in Southerly winds.

Global Circulation Models (GCMs) contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and are widely used in global change research. This paper assesses the performance of the AR4 GCMs in simulating precipitation and temperature in China from 1960 to 1999 by comparison with observed data, using system bias (B), root-mean-square error (RMSE), Pearson correlation coefficient (R) and Nash-Sutcliffe model efficiency (E) metrics. Probability density functions (PDFs) are also fitted to the outputs of each model. It is shown that the performance of each GCM varies to different degrees across China. Based on the skill score derived from the four metrics, it is suggested that GCM 15 (ipsl_cm4) and GCM 3 (cccma_cgcm_t63) provide the best representations of temperature and precipitation, respectively, in terms of spatial distribution and trend over 10 years. The results also indicate that users should apply carefully the results of annual precipitation and annual temperature generated by AR4 GCMs in China due to poor performance. At a finer scale, the four metrics are also used to obtain best fit scores for ten river basins covering mainland China. Further research is proposed to improve the simulation accuracy of the AR4 GCMs regarding China.

This research compared and evaluated the spatio-temporal similarities and differences of eight widely used gridded datasets. The datasets include daily precipitation over East Asia (EA), the Climate Research Unit (CRU) product, the Global Precipitation Climatology Centre (GPCC) product, the University of Delaware (UDEL) product, Precipitation Reconstruction over Land (PREC/L), the Asian Precipitation Highly Resolved Observational (APHRO) product, the Institute of Atmospheric Physics (IAP) dataset from the Chinese Academy of Sciences, and the National Meteorological Information Center dataset from the China Meteorological Administration (CN05). The meteorological variables focus on surface air temperature (SAT) or precipitation (PR) in China. All datasets presented general agreement on the whole spatio-temporal scale, but some differences appeared for specific periods and regions. On a temporal scale, EA shows the highest amount of PR, while APHRO shows the lowest. CRU and UDEL show higher SAT than IAP or CN05. On a spatial scale, the most significant differences occur in western China for PR and SAT. For PR, the difference between EA and CRU is the largest. When compared with CN05, CRU shows higher SAT in the central and southern Northwest river drainage basin, UDEL exhibits higher SAT over the Southwest river drainage system, and IAP has lower SAT in the Tibetan Plateau. The differences in annual mean PR and SAT primarily come from summer and winter, respectively. Finally, potential factors impacting agreement among gridded climate datasets are discussed, including raw data sources, quality control (QC) schemes, orographic correction, and interpolation techniques. The implications and challenges of these results for climate research are also briefly addressed.

Assessing the performance of climate models in surface air temperature (SAT) simulation and projection have received increasing attention during the recent decades. This paper assesses the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating intra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. We evaluate the skill of different multi-model ensemble techniques and use the best technique to project the future SAT changes under different emission scenarios. The results show that most of the general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasia and the difference between the observation and the simulations primarily comes from the winter season. Most of the GCMs can approximately capture the decadal SAT trend; however, the accuracy of annual SAT simulation is relatively low. The correlation coefficient R between each GCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagram shows that the ensemble results generated by the simple model averaging (SMA), reliability ensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to any single GCM output; and the decadal SAT change generated by SMA, REA and BMA are almost identical during 1901–2005. Heuristically, the uncertainty of BMA simulation is the smallest among the three multi-model ensemble simulations. The future SAT projection generated by the BMA shows that the SAT in Northern Eurasia will increase in the 21st century by around 1.03 °C/100 yr, 3.11 °C/100 yr and 7.14 °C/100 yr under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, the spring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP 4.5 scenarios, while the winter season contributes most to the decadal warming occurring under the RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time in the 21st century.

The Asian water tower (AWT) serves as the source of 10 major Asian river systems and supports the lives of ~2 billion people. Obtaining reliable precipitation data over the AWT is a prerequisite for understanding the water cycle within this pivotal region. Here, we quantitatively reveal that the "observed" precipitation over the AWT is considerably underestimated in view of observational evidence from three water cycle components, namely, evapotranspiration, runoff, and accumulated snow. We found that three paradoxes appear if the so-called observed precipitation is corrected, namely, actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. We then explain the cause of precipitation underestimation from instrumental error caused by wind-induced gauge undercatch and the representativeness error caused by sparse-uneven gauge density and the complexity of local surface conditions. These findings require us to rethink previous results concerning the water cycle, prompting the study to discuss potential solutions. [ABSTRACT FROM AUTHOR]

Projections of future hydrological conditions rely largely on global climate models, but model performance varies greatly. In this study, we investigated projected changes in runoff (R), precipitation (P), evapotranspiration (ET), and soil moisture (SM) based on the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) and quantified the uncertainties of their projected changes on annual and seasonal scales. The results indicate that all four hydrological variables show an increase over most of the global land: annual projections of R, P, ET, and SM from CMIP6 increase in 72%, 81%, 82%, and 66% of the global land area, respectively, under a high emissions scenario during the period 2080-99 relative to 1970-99. We estimated the uncertainties in CMIP6 from different sources on an annual scale and found that model uncertainty dominates the total projected uncertainties during the twenty-first century [76% (R), 73% (P), 89% (ET), and 95% (SM) in the 2090s], and the contribution of internal variability decreases with time. The low-latitude regions have the greatest uncertainty in hydrological projections. In CMIP6, the uncertainty of projected changes in P contributes the most to the uncertainty of projected changes in R, with a contribution of 93% on annual scale, followed by ET and SM. Overall, the performances of the CMIP5 and CMIP6 models are similar in terms of hydrological changes and the composition of their uncertainties. This study provides a theoretical reference for the further improvement and development of hydrological components in global climate models. [ABSTRACT FROM AUTHOR]