ABSTRACT The skillful prediction of seasonal extreme precipitation anomalies is especially critical for agricultural planning, water resource allocation and flood mitigation. While single‐model deterministic dynamical systems are inherently limited in prediction skill and uncertainty quantification, the probabilistic predictions of multi‐model ensemble (MME) can effectively improve the prediction performance, integrate diverse physical parameterisations to generate comprehensive probability distributions and quantify climate uncertainty. To address this issue, this study systematically evaluates the probabilistic prediction skill of China Multi‐Model Ensemble (CMME) prediction system for summer extreme precipitation and explores the potential contributors to its advantages. The results indicate that the superiority of MME is more evident in the probabilistic prediction of seasonal extreme precipitation anomalies than in deterministic prediction. This advantage primarily stems from leveraging model diversity in physical processes to enhance prediction reliability, which is particularly evident in tropical regions where large‐scale forcing dominates. In contrast, for mid‐to‐high latitude regions, especially for East Asia, where internal variability is more significant, improving extreme event predictions crucially depends on increasing the ensemble size and the spread of the probability distribution. Furthermore, the optimal probability threshold for translating probabilistic forecasts into deterministic extreme event warnings is identified using the Heidke Skill Score (HSS) across the entire probability range. Based on this optimal threshold, the MME demonstrates stable prediction skill both globally and across key regions. This study confirms the advantages and explores the sources and contributors to the skill improvement of MME in probabilistic predictions of summer extreme precipitation, providing a robust scientific basis for the further objective extraction and utilisation of probabilistic information in seasonal extreme prediction.

Over the past few decades, the Tibetan Plateau (TP) has witnessed changes in summer precipitation that follow a "south drying-north wetting" diploe pattern. Although the role of adjacent oceanic moisture export (OME) in modulating the TP precipitation has been highlighted, there is no consensus on the relationship between OME and summer TP rainfall by far. Here, we quantify the contributions of the OME from three seas, namely, the Arabian Sea (AS), the Bay of Bengal (BOB), and the South China Sea (SCS), to the TP summer precipitation and particularly examine the relationship between them from the standpoint of long-term trend based on the 35-year Lagrangian modeling and OME-based precipitation diagnostic. Climatologically, the observed and the diagnosed OME-based precipitation are compatible, both in terms of spatial pattern and magnitude. However, the preferred regions contributed by the three sea sources vary from one another. The AS with fraction contribution 62.6 % rank first with influenced regions almost covering the whole TP, followed by the BOB (21 %) and the SCS (16.4 %) with impacted regions mainly distributed over the southeastern and eastern TP, respectively. Although this order of contribution to the trends of whole TP precipitation remains unchanged, the AS comparatively acts as a main contributor to the "south drying" over the TP, while the BOB makes more contribution to the "north wetting", implying the distinct mechanisms on the precipitation changes among the TP subsectors.

With global warming, the intensity and frequency of floods have markedly increased, resulting in substantial losses of life and property. The Pearl River basin (PRB) in South China, with its complex topography, remains highly susceptible to flooding. To enhance the precision of flood simulation and forecast in the PRB, an overland flow scheme was first integrated into the community Noah land surface model with multiparameterization options (Noah-MP) and subsequently coupled with the Weather Research and Forecasting (WRF) Model. These models were applied to a record precipitation event occurring over the PRB in April 2024 to validate their improvements. Results reveal that the modified Noah-MP can effectively simulate hydrological processes. The cumulative surface runoff is strongly affected by topography and has a higher magnitude in low-lying areas. The accumulated water depth generally aligns with the satellite-observed inundation, and the error in soil moisture between the model and the observation is reduced. Further, the modified WRF Model has successfully reproduced the inundation area in most regions, contrasting with the original scheme's inability to simulate flooding. In addition, the improvement in hydrological processes in the modified WRF also enhances the ability to simulate precipitation through land-atmosphere interactions. A comparison with the WRF-Hydro simulations further demonstrates that our scheme achieves a certain degree of improvement in simulating inundation. This study presents a promising approach for improving flood simulations in complex topography, which is instrumental in mitigating the loss of life and property caused by flood disasters in the PRB.

Severe precipitation in the Yangtze River Basin (YRB) poses escalating flood risks, underscoring urgent needs for skillful subseasonal prediction. In this study, we develop an integrated dynamical-statistical downscaling model based on overlapping circulation-precipitation co-evolution (OCPCE), which merges prior and concurrent circulation evolution to predict rainfall anomalies. The core innovation shifts from conventional downscaling of dynamical model-predicted circulation to an integrated framework combining observed recent evolution with highly predictable portions of future circulation from dynamical subseasonal-to-seasonal (S2S) models within an optimal overlapping time window. Implemented via evolution-based singular value decomposition, this design maximizes retention of useful initial information while suppressing lower-skill long-lead predictions. The OCPCE model demonstrates statistically significant deterministic skill over YRB and reliable probabilistic predictions at 10-40-day leads, substantially outperforming direct ECMWF-S2S predictions. This work offers a physically coherent and operationally viable framework for improving subseasonal precipitation prediction, providing critical support for early flood warning and proactive disaster prevention.

The Ensemble Prediction System (EPS) provides reliable precipitation forecasts. However, constrained by computational resources, its relatively coarse spatial resolution directly limits its capability to predict high-impact severe rainfall events. Given that downscaling to super-resolution offers a computationally efficient and highly practical solution to enhance forecast resolution, this study develops a Self-Attention-Enhanced Convolutional Neural Network (SAECNN) for downscaling coarse ensemble forecasts of precipitation over North China, an area that has frequently experienced severe rainfall in recent years. The SAECNN integrates a self-attention mechanism and inception-style module. It is trained through a two-step process using pairs of high-resolution (HR) and low-resolution (LR) precipitation data under a composite loss function. The model is trained by using 3-h accumulated summer precipitation data from 2010 to 2019 obtained from the ECMWF ERA5-Land reanalysis dataset. Subsequently, taking the Global Ensemble Prediction System of ECMWF (ECMWF-GEPS) as an example, the SAECNN is applied to the LR GEPS to generate HR precipitation ensemble forecasts. Ablation experiments demonstrate that the combination of Huber loss and mean absolute error with minimized missed rate, along with the two-step training strategy, effectively reduces forecast errors. Independent validation against bilinear-interpolated forecasts of the ECMWF-GEPS during 2020–2021 demonstrates that SAECNN yields realistic and detailed precipitation forecasts, reducing the probabilistic forecast bias (ranked probability score) by 8

The influence of the western Pacific subtropical high (WPSH) and Australian high (AH) on the Indian Ocean Dipole (IOD) phase is investigated in this study. We refer to this influence using the term phase-preferential impacts. The results show that 14 out of 15 IOD events since 1990 have co-occurred with an anomalous WPSH and AH during boreal summer. These circulation anomalies induce variations in the meridional sea-level pressure (SLP) gradient spanning the Maritime Continent (MC), which play a key role in IOD formation. A strong and westward-extended summer WPSH can weaken the meridional SLP gradient over the flanks of MC by increasing SLP over the South China Sea, thereby suppressing monsoonal cross-equatorial southerlies. This reduction in winds decreases both the surface evaporation off Sumatra and coastal ocean upwelling, leading to positive sea surface temperature (SST) anomalies in the southeastern tropical Indian Ocean (TIO) and a subsequent negative IOD. In contrast, when the AH is strong, the subsidence and SLP over the southeastern TIO increase, strengthening the inter-hemispheric pressure gradient and the southeasterlies off Sumatra. These winds enhance evaporation and ocean upwelling, leading to SST cooling in the southeastern TIO, thereby setting the stage for a positive IOD. Conversely, weak AH-induced northwesterlies promote SST warming in this region, facilitating the evolution of a negative IOD. The IOD phases are further strengthened through the excitation of positive air-sea feedback over the TIO. These results highlight the phase-preferential role of the two subtropical highs in modulating the IOD, with a strong WPSH favoring the negative phase and a strong (weak) AH favoring the positive (negative) phase.

Despite global warming, persistent wetu2013cold compound extreme events (PWCEs) during wintertime still occur frequently in China, causing significant disruption of human and social activities. However, their full range, precursors, and evolution characteristics remain unclear. In this study, 252 PWCEs in China during cold seasons from 1961 to 2023 are objectively identified and further classified into the South China (SC), Western China (WC), and Central China (CC) types. Although the frequency of PWCEs has decreased under global warming, they still exhibit high extremity, and the WC-type events even show an upward trend in intensity. The three types of PWCE events share a similar u201Cnorth highu2013south lowu201D meridional pattern over midu2013high latitudes but demonstrate significantly different circulation evolution attributes and precursors: the SC type is dominantly driven by cold-air outbreak linked to the Baikal blockingu2013East Asian trough; the WC type is controlled by the zonal wave train along the South Asian subtropi-cal westerly jet; the CC type is embedded within a well-organized midlatitude great-circle wave train across Eurasia. Their associated moisture transports also show diverse pathways: the SC type primarily harvests moisture from the South China Sea, the WC type from the Indiau2013Burma trough, and the CC type from both the western North Pacific anticyclone and the South China Sea. The precursors for PWCEs originate mainly from the Maddenu2013Julian Oscillation modulating the tropical circulation and moisture transports, especially for the WC and CC types. These findings highlight the diverse formation links and precursors underlying PWCEs in China, offering insights for improving subseasonal prediction of the compound extreme events in China.

Current subseasonal forecasting of extreme temperature in China faces challenges due to relatively low capability of the subseasonal-to-seasonal (S2S) models. Benefiting from ensemble forecasting, the extreme forecast index (EFI) is an effective approach to tackling the challenge; however, it usually requires abundant ensemble members (i.e., dynamic models), while individual S2S models usually have a limited sample of members that constrain their performance in constructing EFI. To address this issue, this study proposes a novel subseasonal EFI approach for temperature utilizing the S2S multi-model ensemble (MME) to provide a much larger ensemble size. This approach, applied to both a bias-corrected MME (MME-C) and a direct MME (MME-D), is evaluated for high temperature scenarios in comparison with the ECMWF single model forecasts. The results show that MME-C, by removing the drifted climatology of individual model and replacing it with observed climatology, can maximally use the unbiased large-sample ensemble information to decently construct EFI. For verification of subseasonal forecast of extremely high temperatures over China, both MME approaches for the EFI construction outperform the ECMWF single model, e.g., threat score (TS) of MME-C can reach 0.52 for 27-day forecasts, and MME-C achieves overall higher skills than MME-D. The decreased skills with lead time exhibit variations across different regions and both of the MME approaches show higher temporal correlation coefficient (TCC) skills in the northern regions such as Northwest and Northeast China and achieve smaller errors across the entire China. Using larger-sample MME information, the MME-C approach proves to be an effective tool for improving subseasonal early warnings of extreme high-temperature risks.

Despite global warming, persistent wet–cold compound extreme events (PWCEs) during wintertime still occur frequently in China, causing significant disruption of human and social activities. However, their full range, precursors, and evolution characteristics remain unclear. In this study, 252 PWCEs in China during cold seasons from 1961 to 2023 are objectively identified and further classified into the South China (SC), Western China (WC), and Central China (CC) types. Although the frequency of PWCEs has decreased under global warming, they still exhibit high extremity, and the WC-type events even show an upward trend in intensity. The three types of PWCE events share a similar “north high–south low” meridional pattern over mid–high latitudes but demonstrate significantly different circulation evolution attributes and precursors: the SC type is dominantly driven by cold-air outbreak linked to the Baikal blocking–East Asian trough; the WC type is controlled by the zonal wave train along the South Asian subtropical westerly jet; the CC type is embedded within a well-organized midlatitude great-circle wave train across Eurasia. Their associated moisture transports also show diverse pathways: the SC type primarily harvests moisture from the South China Sea, the WC type from the India–Burma trough, and the CC type from both the western North Pacific anticyclone and the South China Sea. The precursors for PWCEs originate mainly from the Madden–Julian Oscillation modulating the tropical circulation and moisture transports, especially for the WC and CC types. These findings highlight the diverse formation links and precursors underlying PWCEs in China, offering insights for improving subseasonal prediction of the compound extreme events in China.

Using hierarchical clustering of daily 500-hPa geopotential heights and moisture budget analysis, this study identifies two distinct large-scale atmospheric circulation patterns that primarily govern summer hazard-inducing heavy precipitation over the Tibetan Plateau (TP; hereinafter referred to as TPHP) during 1981-2023. Based on 104 indentified TPHP events, two robust circulation types are distinguished (classification accuracy: 87.5%), comprising 43 Type1 and 61 Type 2 events. Type 1 features a northeastward-shifted East Asian Westerly Jet (EAWJ) and South Asian High (SAH), together with a northwestward-extended Western Pacific Subtropical High (WPSH). This configuration directs moisture from the subtropical western Pacific toward the northeastern TP, where it converges and enhances heavy precipitation frequency, accounting for similar to 98% of events in this pattern. Type2 is characterized by a southeastward-shifted EAWJ and SAH, along with an eastward expansion of the Indian monsoon trough, which favors moisture transport from the Bay of Bengal to the southeastern TP and leads to similar to 80% of events in this pattern. Type 1 is linked to cold sea surface temperature anomalies (SSTAs) in the northern Atlantic and northern Indian Ocean, while Type 2 is associated with warm North Atlantic SSTAs, cold Arabian Sea SSTAs, and La Ni & ntilde;a-like tropical Pacific pattern.

The persistent heavy precipitation associated with the Meiyu front usually features zonal rainbelts with meridional migrations across East Asia in boreal summer and triggers flooding disasters. Its subseasonal forecasting remains inherently challenging, and especially for the rainbelt migration has been paid little attention to before. This study examines the subseasonal forecasting of Meiyu rainbelt meridional migrations and bias correction approach based on the datasets of two subseasonal-to-seasonal (S2S) models, viz., ECMWF and UKMO S2S models. We show that the forecasting skill of pentad-running-mean rainbelts significantly outperforms that of daily rainbelts at lead time over 2–3 weeks through quantifying the latitudinal location of rainbelt (LLR) and its anomaly in the two S2S models and introducing the spatio-temporal correlation coefficient (STCC) as a new skill to represent evolution of LLRs. Results show that the LLR forecasts of ECMWF-S2S have higher skills than those of UKMO-S2S, with a longer lead time reaching the same failure probability and valuable lead time extending up to 30 days by STCC. To overcome systematic forecasting errors in the model climatology, we further propose two reconstruction-based bias-correction approaches for improving the rainbelt forecasts by replacing the climatology of the S2S models with the observed in different stages, which consistently yields higher forecasting skills of LLRs than the original forecasts across all lead times. These findings provide an effective solution for the subseasonal forecasting of the Meiyu rainbelt meridional migrations represented by LLR through the model evaluations and bias corrections.

Recent studies suggest that El Nino-Southern Oscillation (ENSO) can influence Madden-Julian Oscillation (MJO) forecasts, though this relationship remains inadequately examined, especially in boreal summer. Here, we address this gap using multiple dynamical models to systematically evaluate MJO forecast skill across seasons, initial phases, and amplitudes. Results show distinct seasonal dependencies. In boreal winter, MJO forecasts are more skillful during La Nina. This is primarily attributed to improved prediction of phase-2 MJO, which is more intense during La Nina. The phase-2 MJO propagates too fast and encounters an exaggerated Maritime Continent barrier during El Nino, reducing forecast accuracy. The superior prediction of initially strong MJO events during La Nina further boosts forecasts skill. Conversely, during boreal summer, El Nino yields better MJO forecasts than La Nina, stemming from models' ability to accurately capture the eastward-propagating, wavenumber-1 structure of the MJO in phases 3-4 under El Nino. During La Nina, however, westward-propagating disturbances dominate in these phases, which models consistently fail to reproduce, thus lowering forecast skill. Furthermore, in La Nina summers, forecast skill declines with increasing MJO amplitude, especially when initialized from phases 1-3. While models adeptly predict the stationary pattern of low-amplitude MJO, they struggle with the continuous eastward propagation of high-amplitude MJO, partly due to the exaggerated representation of dry, westward-moving signals from the central-eastern Pacific. This work enhances understanding of MJO-ENSO interaction dynamics and has practical implications for subseasonal forecasting.

El Ni & ntilde;o exhibits spatial diversity during its mature stage and is commonly categorized into eastern Pacific (EP) and central Pacific (CP) types. However, contrasting with matured warm sea surface temperature anomaly (SSTA) centers distributed around the Ni & ntilde;o-3.4 region, intense warming or cooling often occurs in the far eastern equatorial Pacific in developing summer, like the 2023 extreme coastal event. Interevent differences of developing El Ni & ntilde;o have been unintentionally overlooked. In this study, two tropical Pacific modes the conventional El Ni & ntilde;o-Southern Oscillation (ENSO) mode and zonal contrast SSTA mode are found to play crucial roles in the diversity of developing El Ni & ntilde;o, as they are comparable factors influencing zonal gradients of equatorial SSTAs. The former features a prominent SSTA center along the Peru-Ecuador coast. The latter features opposite SSTA centers located around 90 degrees W and 180 degrees, respectively. According to the spatial structure in summer, developing El Ni & ntilde;os are classified into two types: the coast-centered (CC) type governed by the ENSO mode and the ocean-centered (OC) type regulated by the zonal contrast SSTA mode. These two types own different dynamic processes, where the CC type is driven by the thermocline feedback (TH), but the OC type is initially dominated by TH, with an increasing contribution of zonal advective feedback over time. The CC and OC developing types can be precursors to the EP and CP mature types, respectively, with approximately 70% of them developing into the corresponding mature types. These results enhance our understanding of ENSO diversity and provide new references for ENSO categorized monitoring.

The modulation of Madden-Julian oscillation (MJO) by El Ni & ntilde;o-Southern Oscillation (ENSO) has been widely investigated, yet the mechanisms particularly the combined influences of ENSO phase (El Ni & ntilde;o/La Ni & ntilde;a) and type (eastern Pacific/central Pacific) remain elusive. Here, we unravel that differences in MJO characteristics (convective intensity, propagation, and structure) between El Ni & ntilde;o and La Ni & ntilde;a are asymmetric across two types of ENSO. These asymmetries are interpreted through established MJO frameworks, involving vertical wind shear, moisture-convection feedback, and structure-propagation nexus. Due to greater interphase differences in low-frequency background winds and humidity, eastern Pacific (EP) ENSO exerts stronger modulation on MJO intensity and northward propagation than central Pacific (CP) ENSO. Weaker MJO modulation of CP ENSO also stems from its distinct regulations of low-and high-frequency MJO components. Moreover, MJO eastward propagation is consistently faster during EP ENSO than during CP ENSO, primarily attributable to stronger premoistening and larger front Walker cell zonal scale leading MJO deep convection, independent of ENSO phases. The MJO propagation over the Maritime Continent also exhibits asymmetry: While MJO events propagate coherently into the central Pacific during both EP ENSO phases, they are frequently blocked during CP La Ni & ntilde;a but not during CP El Ni & ntilde;o. To explain further these propagation asymmetries, we propose five diagnostics based on winds, convection, stratiform heating, and humidity across intraseasonal and low-frequency time scales. Variations in the strength and behavior of these diagnostics under different ENSO backgrounds effectively explain the diversity of MJO propagation. This study advances toward a more comprehensive understanding of how diverse ENSOs modulate the MJO.

Abstract. Rainstorm characterization, which is the fundamental design basis for urban flood control and drainage systems, currently relies primarily on general statistical regularities of heavy rainfall. The current design rainstorm profile (e.g., Chicago hyetograph) overlooks the spatial non-uniformity and unique intensity–duration–frequency (IDF) relationships of typhoon rainfall. This deficiency constitutes a key reason for the systemic failure of urban flood defence engineering when facing extreme typhoon rainfall events. To address this problem, the current study focused on the area of Ningbo in China. Using meteorological station observations, county-level IDF curves for annual maximum typhoon rainfall at specific durations were established, and then the K-means clustering method was applied to extract typical spatiotemporal patterns of typhoon rainfall, which produced the following results. The impact of typhoons in the Ningbo area manifests primarily as extreme rainfall of long duration, with 24-h rainfall being the most notable contributor. Current published IDF curves underestimate the extremes for such prolonged typhoon-related events. Owing to the spatial non-uniformity of typhoon rainfall, marked regional variations of IDF curves are observed across county-level areas. Furthermore, typhoon impacts, as revealed by extension of the study period from 1980–2014 to 1980–2024, exhibited spatially inhomogeneous enhancement, with notable increase in the northern region, reflected primarily in the frequency of extreme events. The extracted temporal rainfall patterns for typhoon events are dominated by the central-peaked pattern (with rainfall concentrated in the middle phase) and the late-peaked pattern, differing substantially from the Chicago hyetograph. The latter exhibits limitations in characterizing the structure of long-duration typhoon-related rainfall because it tends to overestimate peak rainfall intensity. Spatially, rainfall patterns are categorized into dispersed-dominated and concentrated types. Topography is the key driver of local rainfall patterns, dictating the spatial loci and temporal windows in which heavy rainfall develops and suddenly intensifies. Typhoons and their interactions with other weather systems also enhance the local specificity of rainfall patterns. These insights could help in designing realistic typhoon rainfall scenarios for urban flood defence planning.

Abstract The Southern Hemisphere extratropical atmospheric circulation has experienced a strong positive trend in the Southern Annular Mode (SAM). Understanding the cause of this change is crucial for projecting future regional climate change. Here, we analyze the contributing factors to the trend in the SAM under both historical and future conditions. We show that the contribution from tropical Pacific climate trends, marked by a La Niña‐like trend in recent decades (1979–2014), is comparable in magnitude to the effects of greenhouse gases and stratospheric ozone (O3) depletion. The results demonstrate the pivotal role of tropical Pacific trends in the future Southern Hemisphere climate and the need to improve simulations of tropical Pacific climate.

Understanding the dynamics of Madden–Julian Oscillation (MJO) is crucial for enhancing subseasonal–seasonal prediction of global extremes. This study has explored the modulation of Indian Ocean Basin Mode (IOBM) on the MJO during May–August, with minimal concurrent influences of El Niño-Southern Oscillation. Results show that the zonal phase speed of the MJO under the warm IOBM (WI) phase is reduced by approximately 26

The quasi-biennial oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere and influences climate worldwide. This study investigates the seasonal predictability of QBO in eight dynamical forecasting models from international operational centers and organizations. Results show that current dynamical models have high skills in predicting the QBO evolution benefiting from their capability of characterizing the typical downward propagation of QBO. Further analyses show that most models can reasonably reproduce the QBO teleconnection patterns, but with weaker amplitude than observation, which may be due to the underestimation of the Holton-Tan effect in models. Prediction skills of the QBO teleconnections to winter surface climate patterns are not yet satisfactory in some models though they have quite high QBO skills. These results provide a comprehensive evaluation of current status of the QBO dynamical seasonal prediction and thus a deeper understanding of the QBO seasonal predictability, which would shed light on predicting QBO and its teleconnection.