Based on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

Many studies have confirmed that the complexity of a time sequence is closely related to its predictability, but few studies have proposed methods to reduce the time sequence complexity, which is the key to improving its predictability. This study analyzes the complexity reduction method of observed time sequences based on wind speed data. Five sampling methods, namely the random method, average method, sequential method, max method and min method, are used to obtain a new time sequence with a low resolution from a high resolution time sequence. The ideal time sequences constructed by mathematical functions and the observed wind speed time sequences are studied. The results show that the complexity of ideal time series of periodic sequences, chaotic sequences and random sequences increases in turn, and the complexity is expressed by the approximate entropy (ApEn) exponent. Furthermore, the complexity of the observed wind speed is closer to the complexity of a random sequence, which indicates that the wind speed sequence is not easy to predict. In addition, the complexity of sub-time series change with different sampling methods. The complexity of sub-time series obtained by the average method is the lowest, which indicates that the average method can reduce the complexity of observed data effectively. Therefore, the complexity of sub-time series sampled from the high-resolution wind speed data is reduced by using the average method. The method that can reduce the complexity of wind speed substantially will help to choose the appropriate wind speed data, thus improving the predictability.

Understanding the physical mechanisms of compound hot-drought events (CHDEs) over the Yangtze River Basin (YRB) is essential for improving climate predictability. Using high-resolution observations and ERA5 reanalysis, this study conducted a three-dimensional comparative diagnosis of two spatially distinct CHDEs (August 2022 and 2019), deconstructing their circulation dynamics and thermodynamic budgets. Specifically, the 2022 basin-wide event was highly associated with La Niña and a negative Indian Ocean Dipole (NIOD). Anomalous latent heating in the tropical eastern Indian Ocean favored an extensive meridional Hadley circulation and a deep high-pressure belt, blocking southwest moisture transport. Thermodynamically, intense downward vertical motions produced severe descending adiabatic warming, which offset longwave radiational cooling and sustained anomalous heat accumulation throughout the deep troposphere. Conversely, the 2019 localized event occurred under a weak Central Pacific El Niño and a positive IOD. The anomalous tropical cooling weakened systematic subsidence and provided a favorable background for a Rossby wave train, featuring an offshore cyclone over the Western North Pacific that severed moisture pathways. Characterized by shallower vertical subsidence, the lack of a penetrative adiabatic heat source merely maintained a lower-level thermodynamic balance, confining extreme heat to the central–eastern YRB. Although superficially similar at the surface, these CHDEs are sustained by two distinct ocean–atmosphere interaction paradigms. Clarifying these dual paradigms provides a critical physical basis for improving sub-seasonal prediction.