Understanding the spatiotemporal characteristics of regional drought is of great significance in decision-making processes such as water resources and agricultural systems management. The North China Plain is an important grain production base in China and the most drought-prone region in the country. In this study, the monthly standardized precipitation evapotranspiration index (SPEI) was used to monitor the spatiotemporal variation of agricultural drought in the North China Plain from 1960 to 2017. Seven spatial patterns of drought variability were identified in the North China Plain, such as Huang-Huai Plain, Lower Yangtze River Plain, Haihe Plain, Shandong Hills, Qinling Mountains Margin area, Huangshan Mountain surroundings, and Yanshan Mountain margin area. The spatial models showed different trends in different time stages, indicating that the drought conditions in the North China Plain were complex and changeable in the past 58 years. As an important agricultural area, the North China Plain needs more attention since this region shows a remarkable trend of drought and, as such, will definitely increase the water demand for agricultural irrigation. The strong correlation between these spatial distribution patterns indicates that the climate and weather conditions leading to drought are consistent and that drought conditions are independent for regions that are not correlated. If this trend continues, the characteristics of drought variability in the North China Plain will become more complex, and a more detailed water management strategy will be needed to address the effects of drought on agro-ecosystems. Recognizing the drought variability in the North China Plain can provide a basis for agricultural disaster reduction planning and water resources allocation.

As China’s main grain producing region, the Yangtze River basin is vulnerable to changes in wet and dry conditions. In this study, the monthly scale of standardized precipitation evapotranspiration index (SPEI) was calculated, based on the Penman–Monteith equation from 239 meteorological stations in the Yangtze River basin, from 1960 to 2017. Water regime characteristic areas of the Yangtze River basin were extracted and divided using the rotating empirical orthogonal function (REOF). The linear trend of the drought and wetness indicators, the abrupt changes of the rotated principal component time series (RPCs), and the change periods of the drought/wetness intensity (DI/WI) in each subregion were analyzed and discussed. Subsequently, the effects of El Niño-southern oscillation (ENSO) and arctic oscillation (AO) on drought and wetness events were discussed. The results showed that the Yangtze River basin has the characteristic of coexistence of drought and wetness, and drought and wetness of similar severity tend to occur in the same region. There were six subregions extracted through REOF, based on the monthly scale of SPEI, of which the northwestern pattern had an aridization tendency. The stations with significantly increased wetness were located in the middle and eastern basin. The stations in the south of the northwestern pattern, and the west of the southern pattern, had a tendency of wetting in the first 29 years, however, there has been a significant tendency of drying in this region in the last 29 years, which was caused by an abrupt change in 1994. In addition, other patterns had multiple abrupt changes, resulting in multiple transitions between dry and wet states. The principal periods of WI in the southern pattern and northern pattern were longer than the DI, but in other subregions DI was longer than WI. ENSO and AO had the most obvious influence on DI and WI. Compared with the cold phase of ENSO, the DI/WI in the warm phase were higher/lower; compared with the negative phase of AO, both DI and WI were higher in the positive phase. The Hurst index showed that the current dry and wet conditions in the Yangtze River basin have persistent characteristics, the dry conditions in each subregion will continue in the future, and there were a few wetness indicators with weak anti-persistence.