Search

Summer monsoon rainfall forecasting in the Yangtze River basin is highly valuable for water resource management and for the control of floods and droughts. However, improving the accuracy of seasonal forecasting remains a challenge. In this study, a statistical model and four dynamical global circulation models (GCMs) are applied to conduct seasonal rainfall forecasts for the Yangtze River basin. The statistical forecasts are achieved by establishing a linear regression relationship between the sea surface temperature (SST) and rainfall. The dynamical forecasts are achieved by downscaling the rainfall predicted by the four GCMs at the monthly and seasonal scales. Historical data of monthly SST and GCM hindcasts from 1982 to 2010 are used to make the forecast. The results show that the SST‐based statistical model generally outperforms the GCM simulations, with higher forecasting accuracy that extends to longer lead times of up to 12 months. The SST statistical model achieves a correlation coefficient up to 0.75 and the lowest mean relative error of 6%. In contrast, the GCMs exhibit a sharply decreasing forecast accuracy with lead times longer than 1 month. Accordingly, the SST statistical model can provide reliable guidance for the seasonal rainfall forecasts in the Yangtze River basin, while the results of GCM simulations could serve as a reference for shorter lead times. Extensive scope exists for further improving the rainfall forecasting accuracy of GCM simulations.

Owing to their essential influence on human society and natural environment exerted by inducing disasters, such as floods and droughts, further studies on precipitation extremes in China are needed. This study presents the regional frequency and spatial-temporal patterns of precipitation extremes in China based on a high-resolution (0.5° × 0.5°) daily precipitation dataset from 1961 to 2013. With fuzzy c-means, L-moments methods and other scientific statistical tests, a regional frequency analysis (RFA) is conducted, aiming to further understand the regional and spatial distribution of precipitation extremes across China. The results show that: (1) the whole Mainland China can be divided into 50 homogeneous regions on the basis of the characteristics of mean annual precipitation and location indices. (2) For most of the regions, Generalized Extreme Value (GEV), Generalized Normal (GNO) and Pearson type III (PE3) distributions of precipitation extremes fit well, according to the results of goodness-of-fit (GOF) test. (3) For RX1DAY, GEV has the best-fit distribution in the east, northeast and southwest of China, whereas GNO distribution mostly fits the northern and parts of southwest and southeast; in addition, regions which fit PE3 and Generalized Logistic (GLO) distribute dispersedly across the country. (4) For RX5DAY, GEV mainly fits in the middle, southwestern and southern; GNO and PE3 apply best to the northeastern and northern, respectively. (5) Return periods of 20, 50 and 100 years for their best-fit distributions decrease gradually from southeastern China to northwestern China. Compared with the results of GEV distribution fitted to each grid, RFA may provide more accurate estimates of rainfall quantiles. Definitely, the study results will not only benefit further understanding of the unique and complex features of extreme precipitation in the whole Mainland China but also contribute to the nation-scale flood prevention, control and management in the backdrop of the changing climate.

Extremes of weather and climate can cause disasters such as floods and droughts. Further studies of precipitation extremes are crucial for enabling reliable projections of future changes. In this study, we used a high-resolution (0.5° × 0.5°) gridded precipitation data set to analyse these events across the 11 basins in China between 1961 and 2013 mainly by the Mann–Kendall and generalized extreme value methods. Our results indicated a wetter trend in the Tarim River Basin (TRB) but drier conditions in the central Yangtze River Basin (YRB), western Pearl River Basin and eastern Yarlung Tsangpo River Basin. There was an increasing risk of flooding in the mid-lower YRB and Minjiang River Basin (MRB), and an increasing risk of drought in the central Lantsang River Basin. The results revealed strengthened maximum 1-day precipitation (RX1DAY) in the Liaohe River Basin (LB), YRB, MRB and TRB, together with strengthened maximum 5-day precipitation (RX5DAY) in the Songhua River Basin, MRB and TRB during 1987–2013. RX1DAY and RX5DAY for return periods of 20 and 50 years gradually decreased from southeast to northwest. Along the south-eastern coastline of the Mainland and on Hainan Island, the RX1DAY for the 20- and 50-year return periods would exceed 165 and 195 mm, respectively, whereas the RX5DAY would exceed 270 and 320 mm for the same return periods. Furthermore, summer Southern Oscillation Index (SOI) positively impacted precipitation extremes in the Yellow River Basin and YRB, and autumn SOI negatively impacted the extremes of the next year in LB and YRB, while winter SOI negatively impacted the extremes of the next year in YRB. Additionally, the East Asia summer monsoon (EASM) and the South Asia summer monsoon (SASM) were both associated with precipitation extremes across China; however, EASM contributed more to these events than did the SASM.