ABSTRACT Hindered by systematic bias and incapability in capturing the physical modulations of predictability sources, dynamical subseasonal‐to‐seasonal (S2S) models struggle to skillfully predict precipitation beyond 15 days. Based on the CMA‐CPSv3 model, this study introduced three revised QM correction methods (CMs) to improve prediction of daily precipitation in China on the subseasonal timescale. These QM methods comprised a sliding window approach (CM1) and a regional aggregation method (CM2)—both designed to increase sample sizes—and a background‐constrained method (CM3), incorporating the Indian Ocean Basin Mode (IOBM), designed to account for physical influences of potential predictability sources. Results revealed that the raw model bias, including overestimation of drizzle precipitation (≤ 1 mm/day), underestimation of medium to heavy rainfall (≥ 10 mm/day), and misrepresentation of the rain belt along the Yangtze River Basin were improved by the CMs, especially the regional aggregation‐based QM methods (CM2 and CM3). CM3 further improves prediction skill in capturing the spatial distribution pattern of precipitation and reducing the bias, especially under the positive IOBM phase. The superiority of CM3 stems from its ability to accurately capture the spatiotemporal influence of the IOBM on precipitation in China, demonstrating the effectiveness of physical‐background‐constrained bias CM on subseasonal predictions.

ABSTRACT Cloud horizontal heterogeneity within instrument footprints constitutes a major error source in ice cloud retrievals. We present a novel algorithm considering cloud horizontal heterogeneity based on lightweight convolutional neural network (LCNN) designed for Ice Cloud Imagers (ICI), which retrieves high‐resolution ice water path (IWP) by leveraging spatial correlations in low‐resolution satellite observations. Our approach employs a prior database generated through three‐dimensional radiative transfer simulations, containing collocated high‐resolution atmospheric profiles and cloud parameters, as well as the corresponding brightness temperature differences (BTDs). Then, the LCNN is trained and tested by the simulated prior database. Compared to Bayesian Monte Carlo integration (BMCI), the LCNN reduces mean absolute error (MAE) by 12.05 g/m 2 while tripling spatial resolution (from 16 to 5.3 km). This resolution enhancement mitigates errors induced by ice cloud horizontal heterogeneity, achieving a 23.6% reduction in MAE attributing to plane‐parallel cloud assumptions within footprints. The framework demonstrates the viability of deep learning for submillimeter‐wave cloud sensing, providing a pathway to resolve sub‐footprint cloud variability in future satellite missions.

ABSTRACT The far infrared region plays a critical role in the Earth's greenhouse effect. Measurements across this range are limited and previous studies have found it challenging to achieve radiative closure between different instruments across the mid‐far infrared. This study compares high‐resolution downwelling spectra measured from the surface in the Arctic to radiative transfer simulations run with atmospheric profiles from a radiosonde, microwave radiometer, and the 5th ECMWF atmospheric reanalysis. The radiosonde‐driven simulation generates the closest agreement to the radiance observations. The HATPRO‐driven simulation matches within its uncertainty across the entire range. However, it has a larger uncertainty envelope than the other sources. There is a bias in all three simulations associated with underestimated humidity. Our results reiterate the sensitivity of the far infrared to the humidity profile. This suggests that the wider deployment of spectrometers capable of measuring across this region, in parallel with efforts to improve knowledge of far infrared water vapour spectroscopy, could markedly improve our ability to routinely characterise polar environments.

ABSTRACT Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it remains unclear how the performance of these advanced methods depends on the observation network characteristics. In this study, we present initial experiments with the surface quasi‐geostrophic model, in which we compare a recently developed ensemble filter using score‐based diffusion models with the standard Local Ensemble Transform Kalman Filter (LETKF). Our results show that the analysis solutions respond differently to the number, spatial distribution, and nonlinear fraction of assimilated observations. We also find notable changes in the multiscale characteristics of the analysis errors. Given that standard DA techniques will eventually be replaced by more advanced methods, we hope this study sets the ground for future efforts to reassess the value of Earth observing systems in the context of newly emerging algorithms.

ABSTRACT Climate models agree that a reduction of day‐to‐day temperature variability at mid‐to‐high latitudes during the cold season is a robust forced response to anthropogenic global warming. Although recent observations show a similar reduction, how much the observed change is forced and how much is internal variability is uncertain. Here, using large‐ensemble simulations and a Ridge Regression detection tool, we decompose the observed day‐to‐day temperature variability changes since 1950 into contributions of forced and internal components. Our findings show that the observed reduction since the mid‐1970s is dominated by a forced response (about 90%). Observations and models show consistent mechanisms responsible for this reduction in a warming world: the reduction is manifested as cold days warming faster than hot days, driven by Arctic sea‐ice loss and associated reduction in the latitudinal temperature gradient, but not by large‐scale atmospheric circulation changes. Overall, our study detects a robust influence of the Arctic changes on lower latitude day‐to‐day temperature variability, and suggests that this impact will continue in the coming decades.

ABSTRACTThe study of tropical cyclones re‐entering the ocean or making ‘seafall’ has been limited. Here, idealised simulations are used to study the re‐intensification of seafalling tropical cyclones. They follow a two‐stage fast‐slow process driven predominately by a change in surface friction initially and then by heating. The previous land decay causes seafalling tropical cyclones to be larger and intensify more slowly with milder inner‐core contraction than in ocean‐only cases. Nonetheless, they reach the same intensity but with almost twice the integrated kinetic energy, so that the second landfall made by seafalling tropical cyclones can cause more damage due to their larger footprint of destructive wind.

ABSTRACT By using hourly rain gauge records from 1961 to 2020, we find that the annual precipitation differences between highland stations and lowland stations in eastern China are reducing. Specifically, precipitation frequency has decreased significantly in the highlands while precipitation intensity has increased considerably in the lowlands, both contributing to a reduced elevation–dependency of precipitation. The decreasing precipitation frequency correlates with reduced convective available potential energy (CAPE), whereas the increasing precipitation intensity is linked to enhanced low‐level convergence.

ABSTRACTChina faces the Northwest Pacific with the world's most active tropical cyclones (TCs). Whether and how the warming of the “Roof of the World”, the Tibetan Plateau (TP), influences the environmental field and precipitation of landfalling TC (LTC) remains unclear. In this study, a data‐driven objective classification reveals that the key environmental field influencing the LTC precipitation in China is closely related to the TP‐related high pressure. The precipitation rate of LTC in China exhibits an overall increasing trend over the past 43 years under TP warming. However, the trend of the precipitation rate depends on environmental fields. For LTCs affecting southeastern China, the South Asian High (SAH) intensifies and extends eastward, the Western Pacific Subtropical High (WPSH) shifts westward but weakens, stabilizing the atmosphere and reducing precipitation rate. For LTCs impacting southern China, the SAH and WPSH strengthen, increasing atmospheric instability and enhancing precipitation rate.

ABSTRACTIn addition to the global mean surface temperature (GMST), accurately predicting regional features associated with GMST changes is essential for effective climate policymaking at the regional level. This study investigates regional patterns of surface temperature and precipitation associated with interannual GMST variability and evaluates their representation in state‐of‐the‐art climate models. Although the influence of the El Niño‐Southern Oscillation (ENSO) on GMST variability is well recognized, it accounts for less than 50% of the total interannual variability. A significant portion of GMST warming, independent of ENSO, is closely associated with widespread regional warming—most notably over North America—along with increased atmospheric river activity and enhanced precipitation along its west coast. However, current climate models systematically underestimate the ENSO‐independent component of GMST variability, thereby introducing uncertainties into our interpretation of GMST changes.

ABSTRACTSeasonal prediction of atmospheric rivers (ARs) in the western north Pacific (WNP) is examined using a seasonal prediction model with and without atmospheric initialisation. A 20‐year seasonal prediction was conducted to evaluate the model's prediction skill, particularly focusing over the Japan area. The prediction skill of the present model indicated that the seasonal AR frequency is predictable with a lead time of up to 7–10 months, and the atmospheric initialisation further improved the skill. An additional investigation was conducted to identify the source of predictability for seasonal ARs. One significant source is the predictability of the Pacific‐Japan (PJ) pattern, which is influenced by the model's skill in predicting tropical sea surface temperature (SST) variability. The anticyclonic circulation southeast of Japan is well predicted when the tropical SST variability and PJ pattern are accurately predicted. Another source of predictability difference originated from the subsurface sea temperature (SBT) beneath the subtropical high in the North Pacific. When the SBT prediction is improved with atmospheric initialisation, it enhances the air‐sea interactions over the subtropical high in the WNP and southeast of Japan, leading to better predictability of seasonal ARs.