Spatiotemporal behaviors of meteorological droughts are basic for understanding the development and driving forces of droughts. Currently, there are a limited number of approaches mainly used to analyze the spatiotemporal evolution of large‐scale soil moisture/hydrological drought events at global or continental scales. These behavior description approaches on the spatiotemporal dynamics of drought are not perfect and should be improved further. To this end, a new approach was proposed to investigate the migration of meteorological droughts based on topological spatial relations. It was used to identify drought clusters, migration trajectories, and migration directions of meteorological droughts. In the Poyang Lake basin (24°29′ to 30°04′N, 113°34′ to 118°28′E), China, monthly precipitation data were available from 1960 to 2007. The one‐month standardized precipitation index was used to characterize meteorological droughts. The proposed approach identified a total of 77 drought events and 454 drought clusters during the study period. Their drought centroids were tracked and analyzed. The migration distances of drought events exhibited a significant linear relationship with the drought duration (R² > 0.8, slope of 165 km per month). More than 70% of drought events had a migrating displacement greater than 1° (approximately 100 km). These results indicated that the meteorological drought events traveled actively and occurred on a widespread basis in the basin. Moreover, two main patterns of drought cluster migration (northward and southward) were identified. The approach and findings can provide some important implications to investigate the migration of meteorological droughts in other regions.

Temperature forecasting is a crucial part of climate change research. It can provide a valuable reference, as well as practical significance, for understanding the macroscopic evolutionary processes of regional temperature and for promoting sustainable development. This study presents a new integrated model, called the Variational Mode Decomposition-Autoregressive Integrated Moving Average (VMD-ARIMA) model, which reduces the required data input and improves the accuracy of predictions, based on the deficiencies of data dependence and the complicated mechanisms associated with current temperature forecasting. In this model, the variational mode decomposition (VMD) was used for mining the trend features and detailed features contained in a time series, as well as denoising. Moreover, the corresponding autoregressive integrated moving average (ARIMA) models were derived to reflect the different features of the components. The final forecasted values were then obtained using VMD reconstruction. The annual temperature time series from the Wuhan Meteorological Station were investigated using the VMD-ARIMA model, ARIMA model, and Grey Model (1, 1) based on three statistical performance metrics (mean relative error, mean absolute error, and root mean square error). The results indicate that the VMD-ARIMA model can effectively enhance the accuracy of temperature forecasting.

Meteorological drought monitoring is important for drought early warning and disaster prevention. Regional meteorological drought can be evaluated and analyzed with standardized precipitation index (SPI). Two main processing schemes are frequently adopted: (1) mean of all SPI calculated from precipitation at individual stations (SPI-mean); and (2) SPI calculated from all-station averaged precipitation (precipitation-mean). It yet remains unclear if two processing schemes could make difference in drought assessment, which is of significance to reliable drought monitoring. Taking the Poyang Lake Basin with monthly precipitation recorded by 13 national stations for 1957–2014, this study examined two processing schemes. The precipitation mean and SPI mean were respectively calculated with the Thiessen Polygon weighting approach. Our results showed that the two SPI series individually constructed from two schemes had similar features and monitoring trends of regional meteorological droughts. Both SPI series had a significantly positive correlation (p < 0.005) with the number of precipitation stations. The precipitation-mean scheme reduced the extent of precipitation extremes and made the precipitation data more clustered in some certain, it made less precipitation deviate from the precipitation-mean series farther when less precipitation occurred universally, which would probably change the drought levels. Alternatively, the SPI-mean scheme accurately highlighted the extremes especially for those with wide spatial distribution over the region. Therefore, for regional meteorological drought monitoring, the SPI-mean scheme is recommended for its more suitable assessment of historical droughts.