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This study investigates the interannual variability of summer precipitation over Central Asia and explores its precursory signals through soil moisture (SM) and sea surface temperature (SST) anomalies. The results reveal that southern Central Asia (SCA) is a crucial SM‐precipitation coupling region where summer precipitation is significantly linked to the preceding April SM in the Turan Plain, between the Caspian Sea and the Tibetan Plateau. The preceding Turan Plain SM (TPSM) anomaly can reflect the ensuing summer SM anomalies in the SCA region due to the persistence of SM. The higher TPSM can stimulate anomalous convective ascent and associated negative geopotential height anomalies over the SCA region, which is favourable for more SCA precipitation (SCAP) during summer. Additionally, Indian and Pacific Ocean SST (IPOS) anomalies can regulate the summer SCAP through ocean and land relay effects. The preceding April can reflect the subsequent summer SST anomalies across the northern Indian Ocean and South China Sea, which trigger a Matsuno‐Gill response and induce anomalous water vapour transport into the SCA region, providing favourable moisture conditions for more SCAP during summer. Apart from such an ocean relay effect, the preceding IPOS can modulate the April TPSM first and then affect the summer SCAP through the land relay effect caused by persistent SM anomalies from April to summer. The combined effect of the preceding TPSM and IPOS on the summer SCAP is more pronounced than the respective effect of either TPSM or IPOS. Incorporating the TPSM signal as a supplementary precursor can enhance the predictive skill of summer SCAP. These findings may provide valuable insights into the reasons and prediction of the variability of summer SCAP. [ABSTRACT FROM AUTHOR]

The 0–1 mixed integer programming problem of binary offloading on wireless powered mobile edge computing (WP‐MEC) networks requires joint optimization of binary and continuous variables, which is computationally expensive for traditional techniques and difficult to solve within the channel coherence time under time‐varying condition. Using machine learning models to output variable values is also challenging. Hence, designing efficient and low‐complexity algorithms is crucial for optimal network performance. This paper focuses on the computation latency of the FDMA‐based WP‐MEC network and proposes a task‐offloading algorithm to minimize the total completion delay (TCD). The TCD minimization is modelled as a 0–1 MIP problem and is decomposed into a master problem of optimizing the offloading decision and the sub‐problem of optimizing other parameters under a given offloading decision. The sub‐problem is solved using optimization method, while the master problem is solved using a deep reinforcement learning algorithm. Simulation results show that the proposed algorithm can achieve almost minimal TCD with low complexity. [ABSTRACT FROM AUTHOR]