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Compared to interannual and interdecadal variations, inadequate attention has been given to intraseasonal changes in the summer northwest Pacific subtropical high (NWPSH). During 1981–2005, the north Indian Ocean (NIO) and NWPSH exhibit significant relationships from June to August, while the influences of the Boreal Summer Intraseasonal Oscillation 1 (BSISO1) in June and August differ from its impacts in July, resulting in a significant correlation between the NWPSH in June and August. The models capture NIO's roles but underestimate BSISO1's influences, leading to an underestimation (overestimation) of the relationship between NWPSH in June and August (adjacent two months). A dynamical‐statistical approach based on the close relationship between June and August NWPSH is proposed to improve models' prediction capabilities. Improved results show the mean correlation coefficients (root mean square errors) between prediction and observation for August precipitation and geopotential height increase by 57% and 21% (decrease by 0.27 mm/day and 1.61 m). Plain Language Summary: The Northwest Pacific Subtropical High (NWPSH) holds significant importance for the East Asian summer climate as it directly affects precipitation and temperature. Accurate predicting the NWPSH in advance is crucial, but it poses challenges at subseasonal scales as it requires considering both initial fields and external forcing factors. This study primarily evaluates and enhances the prediction capabilities of Seasonal‐to‐Subseasonal (S2S) models for the relationship between the summer NWPSH in different months. Due to the combined influences of the boreal summer intraseasonal oscillation 1 (BSISO1) and the north Indian Ocean (NIO), the NWPSH in June exhibits a strong correlation with the August NWPSH. Therefore, based on the connection between the June and August NWPSH, this paper proposes a dynamical‐statistical approach aimed at enhancing the prediction capabilities of S2S models for August precipitation and geopotential height. Key Points: Intraseasonal oscillation and north Indian ocean together cause high correlation between June and August northwest Pacific subtropical highThe models can only capture the influences of the north Indian ocean but not the intraseasonal oscillationThe models' prediction skills for August precipitation are markedly improved when referring to the June northwest Pacific subtropical high [ABSTRACT FROM AUTHOR]

In this study, we compared two anomalous wet February periods in 2021 and 2022 in China. The same anomalies appeared in the spatial distribution of precipitation, with anomalous precipitation centered over the southeast coast. However, temperature discrepancies appeared in most of China, with anomalously high temperatures in 2021 and lower temperatures in 2022. Both instances of increased precipitation were attributed to warm and moist advection from the south, with transport in 2021 being partly enhanced by the South China Sea cyclone, whereas transport in 2022 was mainly due to the subtropical western North Pacific anticyclone. Therefore, in this study, we aimed to compare and analyze temperature and precipitation anomalies in February 2021 and 2022 using the self-organizing map method. Warm events in East Asia and cold events in Siberia and the Tibetan Plateau types were obtained by mode 1, which contained 2021. Mode 6 exhibited opposite warm types in Siberia and cold types in southern Asia, including February temperature and precipitation anomalies in 2022. Based on the results of this study, we can conclude that precipitation anomalies in February 2021 and 2022 occurred under different temperature and circulation anomalies, and both were influenced by La Niña events. Autumn sea ice loss in the Barents Sea contributed significantly to warm and rainy events in February 2021. However, the cold and rainy events of February 2022 were closely related to the strengthening of the Siberian High. [ABSTRACT FROM AUTHOR]

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

The Pacific‐Japan (PJ) pattern is the dominant teleconnection over East Asia, closely related to the summer precipitation on various time scales. However, studies on the connection between the interdecadal variations of the PJ pattern and sea surface temperature (SST) forcing are still limited. This study indicates that the relationship between the PJ pattern and ENSO presents a pronounced interdecadal change around 1997/1998. The PJ pattern shows a baroclinic structure over the northwest Pacific and a barotropic structure near Japan during 1979–1997, while its baroclinic structure in the tropics weakens remarkably during 1998–2021, with a southward shift of its midlatitude barotropic structure. As a result, the connection between the PJ pattern and summer precipitation over East Asia exhibits an interdecadal transition in the late 1990s. In the slow La Niña decaying summer of 1979–1997, the cold SST anomalies in the tropical Indian Ocean (TIO) excite an eastward‐propagating Kelvin wave that enhances convection over the northwest Pacific, resulting in the formation of the PJ pattern. In contrast, the cold SST anomalies in the Maritime Continent rather than the TIO yield a zonally confined Kelvin wave in the rapid La Niña decaying summer of 1998–2021. The elongated Kelvin wave penetrating the central Pacific weakens convective anomalies over the northwest Pacific, thereby weakening the baroclinic structure of the PJ pattern in the tropics. Meanwhile, the warm SST anomalies in the Kuroshio Extension (KE) induce a northward shift of the East Asian subtropical westerly jet, with the anomalous anticyclonic circulation over the KE significantly affecting summer precipitation along the Meiyu belt. Therefore, the midlatitude barotropic structure of the PJ pattern shifts southward after the late 1990s. [ABSTRACT FROM AUTHOR]

Arid ecosystems are known to be sensitive to climate change. The Jornada Basin in the USA, as one representative of arid land, has suffered from land degradation in recent decades. In order to disentangle the climate–vegetation feedback, we analyzed the vegetation dynamics under the effects of climate change via a mathematical model based on the reaction–diffusion mechanism. Using this model, we conducted a sensitive analysis of climate factors and concluded that the ecosystem might experience a catastrophic shift with the climatic deterioration. We considered the non-local interaction term to explain the competition among plants. Additionally, the PLR (power law range) metric was used to quantify the extent of the degradation and to compare the results of the vegetation patterns from the remote sensing data and the simulations. From the results, this model could simulate the trends of land degradation in this area. We found that the land degradation could be mainly attributed to climate changes in recent years. This approach suggests that vegetation patterns can provide hints as to whether the ecosystem is approaching desertification. These results can help with mapping vulnerable arid areas around the world through model simulation and satellite images. [ABSTRACT FROM AUTHOR]