The climate change impact on hydrology in China’s Huang-Huai-Hai (H-H-H) region was assessed in this study. Both variations in mean monthly and annual runoff and occurrences of extreme events including flood and drought were examined for two future periods (2001–2030 and 2016–2045) in the whole region. The projected daily maximum and minimum temperature and precipitation from the PRECIS (providing regional climates for impacts studies) regional climate model were used to drive the variable infiltration capacity (VIC) hydrology model. Variable infiltration capacity was run over a regional domain of 408 grid points at a spatial resolution of 50×50 km. The result shows that PRECIS projects increase in both future temperature (0.8–1.5°C) and precipitation (3.5–7.3%) in the H-H-H region under A2 and B2 scenarios of the fourth Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES). Over the entire H-H-H region, VIC projects increase 11.3 and 13.7% in mean annual r...

Based on Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset and a variable infiltration capacity (VIC) hydrological model, this study assesses the possible influence of climate change in the upstream region of the Yangtze River on droughts in the future 30 years. Long-term daily soil moisture content were simulated by VIC model at a 50 km × 50 km resolution from 1951 to 2013. Regional historical drought events were then recognized based on soil moisture anomaly percentage index and validated with field data. Five relatively independent representative global circulation models were selected and the outputs of them were downscaled temporally and spatially as the inputs of VIC model for daily soil moisture content simulations both in the period of 1971–2000 for the present-day climate and in the period of 2021–2050 for the future. The results show that the projected annual mean temperature is likely to increase from 1.4 °C to 1.8 °C. The projected change in mean annual precipitation could be increased slightly by 0.6% to 1.3%, but the trend of precipitation change in summer and autumn might be opposite of that. Comparing the drought characteristics values recognized in 1971–2000, seven to eight additional regional drought events are likely to happen in 2021–2050. Drought duration and drought intensity are also likely to extend for 18 d to 25 d and increase by 1.2% to 6.2%, respectively. But, drought area could decrease slightly by 1.3% to 2.7% on average. These changes in drought characteristics values suggest that regional drought could become more severely prolonged and frequent in future.

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

A small change in the mean climate may lead to a dramatic change in the frequency and intensity of extreme climate events. In this study, the relationship between mean temperature (MT) and extreme precipitation and the influence from large-scale circulation were investigated in Hunan Province. The correlation between MT and the frequency of extreme precipitation events in different seasons (spring, summer, and autumn) and time periods (1960–2009) was used to obtain pairs of spatial patterns by the singular value decomposition method. The temporal expansion series displayed a consistent trend of temperature and extreme precipitation, and a mutation was observed to occur approximately during the 1980s–1990s. Temperature exhibited a warming trend after the mutation, but the frequency of extreme precipitation events exhibited obvious spatio-temporal variations. The causes of seasonal differences in the frequency of extreme precipitation events were determined by comparing interdecadal changes in three atmospheric circulation factors (850 hPa winds, the entire layer of vapor transportation fluxes and vapor flux divergence) before and after the mutation was revealed.

Climate change can impact the yield and water footprint of crops. Therefore, assessing such impacts carries great significance for regional water and food security. This study validated and verified the variety parameters of winter wheat for the Decision Support System for Agrotechnology Transfer (DSSAT) model, using the long-term (1993–2013) growth and yield data observed from six agricultural experiment stations in the Haihe River Basin (HRB), China. The growth process was simulated under three representative concentration pathways (RCPs), named RCP2.6, RCP4.5, and RCP8.5—climate scenarios driven by the HadGEM2-ES model. The variety parameters of winter wheat showed high accuracy in the simulation of the anthesis and maturity dates, and could be used for long-term prediction of the growth process. The trends of climate change had positive impacts on the water footprint of winter wheat but adverse impacts on the yield. The growing period was shortened by 3.6 days, 4.7 days, and 5.0 days per decade in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, due to the rapid accumulation of heat. The yield would be increased in lower emissions scenarios (17% in RCP2.6), but decreased in high-emissions scenarios due to high temperatures, which may restrict the growth of wheat. The water footprint was decreased by 10%, 11%, and 13% in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, indicating that the water-use efficiency could be improved in the future. The results showed broad application prospects of the DSSAT model in simulating the response of crop growth to climate change.

This study evaluates the performance of Global Climate Models (GCMs) in simulating extreme climate in three northeastern provinces of China (TNPC). A total of 23 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected and compared with observations from 1961 to 2010, using the 12 extreme climate indices defined by the Expert Team on Climate Change Detection and Indicators. The Interannual Variability Skill Score (IVS), Taylor diagrams and Taylor Skill Scores (S) were used as evaluation tools to compare the outputs of these 23 GCMs with the observations. The results show that the monthly minimum of daily minimum temperature (TNn) is overestimated in 55.7% of the regional grids, while the percentage of time when the daily minimum temperature is below the 10th percentile (TN10p) and the monthly mean difference between the daily maximum and minimum temperatures (DTR) are underestimated in more than 95% of the regional grids. The monthly maximum value of the daily maximum temperature (TXx) and the annual count when there are at least six consecutive days of the minimum temperature below the 10th percentile (CSDI) have relatively low regional spatial biases of 1.17 °C and 1.91 d, respectively. However, the regional spatial bias of annual count when the daily minimum temperature is below 0 °C (FD) is relatively high at 9 d. The GCMs can efficiently capture temporal variations in CSDI and TN10p (IVS < 0.5), as well as the spatial patterns of TNn and FD (S > 0.8). For the extreme precipitation indices, GCMs overestimate the annual total precipitation from days greater than the 95th percentile (R95p) and the annual count when precipitation is greater than or equal to 10 mm (R10 mm) in more than 90% of the regional grids. The maximum number of consecutive days when precipitation is below 1 mm (CDD) and the ratio of annual total precipitation to the number of wet days (greater than or equal to 1 mm) (SDII) are underestimated in more than 80% and 54% of the regional grids, respectively. The regional spatial bias of the monthly maximum consecutive 5-day precipitation (RX5day) is relatively small at 10.66%. GCMs are able to better capture temporal variations in the monthly maximum 1-day precipitation (RX1day) and SDII (IVS < 0.6), as well as spatial patterns in R95p and R10mm (S > 0.7). The findings of this study can provide a reference that can inform climate hazard risk management and mitigation strategies for the TNPC.