ABSTRACTIn September 2024, the Yangtze River basin experienced a supremely extreme heatwave that broke historical records from at least 1961 and could have a severe impact on outdoor health of school children. This paper provides a timely analysis of the characteristics of the extreme heatwave in the Yangtze River basin in September 2024, its exposure to the population aged 14 years and below, and the causes that led to its occurrence, as well as its future projections. In September 2024, the regional average heatwave days in the Yangtze River basin reached 7.57 days, and the average daily maximum temperature (Tmax) reached 31.53°C, both of which are much higher than the climatology and exceed the historical records. This supremely heatwave resulted in high exposure of the population aged 14 years and under, with the provinces of Sichuan, Chongqing, Hunan, and Jiangxi exposed to more than 100 million person‐days. The extreme expansion of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH) may have directly contributed to this supremely heatwave. The CMIP6 projections show that the frequency of extreme heatwaves in September similar to that in 2024 will increase in the future.

Abstract With analysis of local climate zone (LCZ) classification, approximately 52.0% of underlying surfaces in Beijing are covered by buildings with LCZ 5 (open midrise) accounting for the highest proportion, and LCZ D (low plants) is the most distributed among natural surface types. Compared to natural underlying surfaces, building underlying surfaces have higher values in the high temperature (HT) and heat wave (HW) days, HW intensity, and maximum HW duration. In recent decades, HT days on building underlying surfaces in Beijing start earlier and end later than those on natural underlying surfaces. Building underlying surfaces make greater contribution to urban heat island intensity of apparent temperature than to that of temperature, yet it is opposite for natural underlying surfaces.

Abstract The Pacific–Japan (PJ) teleconnection pattern, a dominant mode of atmospheric variability over the western North Pacific during boreal summer, is pivotal in shaping regional climate dynamics. Despite its important implications, accurately predicting the PJ pattern remains challenging due to inherent model biases and uncertainties. This study delves into the impact of model biases on the prediction skill of the PJ pattern and evaluates its predictability using outputs from three operational seasonal forecast models. Our findings elucidate that the spatial structure of the PJ pattern simulated by models introduces substantial diversities in prediction skills. By discerning the variance in PJ teleconnection simulation among models, we unveil the high predictability of the PJ pattern, showcasing its capability for accurate forecasts up to 3 months in advance within the current seasonal forecast models. The predictability of the PJ pattern stems from concurrent El Niño–Southern Oscillation‐related sea surface temperature anomalies and its corresponding atmospheric teleconnection processes. Our research underscores the necessity of accounting for model biases in predicting the PJ pattern, and the potential for bolstering seasonal prediction skill through targeted mitigation of these biases.

Abstract In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year.

Abstract North China experienced an extreme precipitation event from July 29 to August 1, 2023 (i.e., the “23.7” event) causing severe floods, significant infrastructure damage and multiple fatalities. To enhance comprehension of the mechanism behind the extreme precipitation of the “23.7” event, water vapor transport paths and sources were determined, and water vapor contribution of each source was quantitatively evaluated based on Lagrangian methods. Results showed that the extreme precipitation of the “23.7” event was closely related to large‐scale water vapor transport and convergence from low‐latitude oceans. There were five main water vapor sources which corresponded to five transport pathways. Path 1 was derived from tropical West Pacific, containing the most trajectories (195), carrying the most water vapor (69.3%) and contributing the most to the extreme precipitation of the “23.7” event (45.7%). Path 2 was guided by the cross‐equatorial flow through South China Sea, contributing to 10.1% of the precipitation. Path 3 originating from eastern tropical Indian Ocean and Path 4 from the west source near the Caspian Sea contributed less to the precipitation. Last but not the least, water vapor evaporation from eastern China contributed more than 30% to the extreme precipitation, making this region another important water vapor source.

Abstract This letter raises the possibility that ergodicity concerns might have some bearing on the signal‐to‐noise paradox. This is explored by applying the ergodic theorem to the theory behind ensemble weather forecasting and the ensemble mean. Using the ensemble mean as our best forecast of observations amounts to interpreting it as the most likely phase‐space trajectory, which relies on the ergodic theorem. This can fail for ensemble forecasting systems if members are not perfectly exchangeable with each other, the averaging window is too short and/or there are too few members. We argue these failures can occur in cases such as the winter North Atlantic Oscillation (NAO) forecasts due to intransitivity or regime behaviour for regions such as the North Atlantic and Arctic. This behaviour, where different ensemble members may become stuck in different relatively persistent flow states (intransitivity) or multi‐modality (regime behaviour), can in certain situations break the ergodic theorem. The problem of non‐ergodic systems and models in the case of weather forecasting is discussed, as are potential mitigation methods and metrics for ergodicity in ensemble systems.

Abstract The Aeroclipper is a new balloon device that can be attracted and captured by tropical cyclones (TC) and perform continuous in situ measurements at the air–sea interfaces. To estimate the potential effect of Aeroclipper observations on the analysis of TCs, virtual Aeroclipper observations targeting TC Haima (October 2016) were synthesized using an idealized surface pressure distribution and best track data and were assimilated using an ensemble data assimilation system. Results show that the assimilation of Aeroclipper measurements may provide a more accurate representation of the TC pressure, wind, and temperature in analyses. This also leads to improved precipitation around the Philippines. The ensemble spread shows that the Aeroclipper measurement assimilation has an impact on the analyses that extends into the tropics from the early stages of TC development. These impact signals propagate westward with easterly waves and eastward with large‐scale convective disturbances. Although the underlying mechanisms need to be further examined and tested using real Aeroclipper measurements, the present study shows that these balloons could provide valuable observations to improve the precision of analyses in presence of a TC. This is a first step toward a study of the impact of the Aeroclipper measurement on TC forecast.