From 19 July 2022 to 31 August 2022, a rare persistent drought and heat event occurred in the middle of the Yangtze River basin (MYRB). Normalized difference vegetation Index (NDVI) over 25% of the area decreased more than 0.05 compared with the climatology, causing extremely agricultural drought disaster and economic losses to China. Previous studies have shown that the occurrence of compound drought and heat events (CDHEs) in the MYRB was associated with intra-seasonal oscillations (ISOs) from different latitudes. Nevertheless, what was the role of ISOs at different latitudes in the formation of the CDHE? To address this question, this paper designed a numerical simulation experiment of partial lateral forcing to investigate the changes in meteorological elements by removing the signals of ISOs on different lateral boundaries. We found that the wave series formed in the upper troposphere at 200 hPa played a significant role in the occurrence of the CDHE in the northern part of the MYRB in this progress. It was found that the ISO component of the northern boundary caused the mean temperature to rise by 2.4 °C and aggravated the drought in 53.7% of the region. On the other hand, the anticyclone anomaly in the lower troposphere at 800 hPa had a continuous impact on the southern and eastern boundaries. It was found that the ISO component of these two boundaries can increase the average temperature by 1.93 °C in the MYRB and intensify the drought in 49.7% of the area. In the developing period of the CDHE, the South Asian high and the Western North Pacific subtropical high were coupled with each other and jointly controlled the MYRB, so that the significant positive geopotential height anomaly stayed above the MYRB for a long time, which was conducive to the development of local subsidence. The results of this paper will help to better understand the formation mechanism of CDHEs in the MYRB and assist meteorologists to prevent and forecast the occurrence of CDHEs in advance.

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Abstract. As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and recent deep learning models for subseasonal forecasts, as forecast times increase, forecast results tend to become intraseasonal low-frequency components, which are essential to the change in general circulation on the subseasonal timescale as well as persistent extreme weather. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the model used in this paper is better than that of the model trained directly with original data. Compared with other models/methods, the SE-ResNet model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the CFSv2 results have a better prediction performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 day, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting 3–8 planetary waves, which leads to the difference in model prediction performance in extratropical areas. A case study shows that the SE-ResNet model depicted the phase change and propagation characteristics of planetary waves well. Thus, the application of data- driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts.