Mu-Ting Chien,Wenchang Yang,Eric D. Maloney,Gabriel A. Vecchi,Elizabeth A. Barnes
{"title":"Deep-learning climate emulator ACE2 reveals a global decrease in 15th-century tropical cyclone frequency under El Niño-like sea surface temperatures","authors":"Mu-Ting Chien,Wenchang Yang,Eric D. Maloney,Gabriel A. Vecchi,Elizabeth A. Barnes","doi":"10.1038/s41612-026-01562-x","DOIUrl":"https://doi.org/10.1038/s41612-026-01562-x","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"33 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A recent doubling of global warming rate signals a 1.5 °C breach by 2028","authors":"Qinjian Jin,Bing Pu","doi":"10.1038/s41612-026-01561-y","DOIUrl":"https://doi.org/10.1038/s41612-026-01561-y","url":null,"abstract":"","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"26 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jing Lei, Yingying Sha, Xinzhou Li, Heng Zhang, Zhengguo Shi
{"title":"Modulation of precession-forced Asian–African monsoon and arid climate variability by obliquity","authors":"Jing Lei, Yingying Sha, Xinzhou Li, Heng Zhang, Zhengguo Shi","doi":"10.1038/s41612-026-01566-7","DOIUrl":"https://doi.org/10.1038/s41612-026-01566-7","url":null,"abstract":"Precession and obliquity play vital roles in orbital-scale climate change. However, their combined effects remain poorly understood. In this study, we examine how obliquity modulates precession-driven climate responses across Asian–African monsoon and arid regions (AAMAR). Results demonstrate that under high obliquity, the minimum-precession-induced precipitation increase is weakened in the Asian–African monsoon regions but enhanced in the Asian–African arid regions. The changes in precipitation responses over Africa and West Asia are largely explained by an enhanced northward displacement of the regional Hadley circulation, which shifts the zone of precipitation increase northward from monsoon regions into adjacent northern arid regions. Additionally, intensified negative cloud-radiative feedback and adiabatic cooling under high obliquity contribute to attenuated Tibetan Plateau heating, further modulating hydroclimate responses over Asia. Overall, our findings reveal that obliquity modulates low-latitude climate sensitivity to precession and provide new insights into the spatially heterogeneous responses of the climate system to orbital forcing.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"3 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seo-Young Jo, Jeong-Hwan Kim, Daehyun Kang, Jae-Heung Park, Seung-Ki Min, Minju Kim, Mi-Kyung Sung
{"title":"Explainable Deep Learning Reveals Future Changes in East Asian Extreme Winter Predictability","authors":"Seo-Young Jo, Jeong-Hwan Kim, Daehyun Kang, Jae-Heung Park, Seung-Ki Min, Minju Kim, Mi-Kyung Sung","doi":"10.1038/s41612-026-01564-9","DOIUrl":"https://doi.org/10.1038/s41612-026-01564-9","url":null,"abstract":"Deep learning-based approaches have emerged as powerful tools for seasonal prediction, yet their skill remains limited for extreme anomalies, and warming climates may alter predictability sources. Here we present a convolutional neural network (CNN)-based framework to assess how seasonal predictability changes in a future climate, focusing on East Asian extreme winters. Our CNN, trained on CESM2-LENS simulations for historical and SSP3-7.0 scenarios and optimized with Amplitude Focal Loss, reliably captures seasonal mean and extreme temperatures. Overall prediction skill increases in the future climate, with warm winter extremes becoming more predictable while cold extremes show little change. Using Integrated Gradients, an explainable AI method, we show this future asymmetry is linked to enhanced sensitivity of East Asian winter temperatures to tropical Pacific SST under background warming, where warm-phase SST more effectively drives extratropical circulation changes. Our framework provides a useful tool for exploring how seasonal predictability mechanisms evolve under climate change.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"58 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Twenty years of Kilimanjaro observations across 5000 m reveal tropical elevation-dependent warming under clear-sky conditions","authors":"Yaping Mo, Nick Pepin, Harold Lovell, Yongming Xu","doi":"10.1038/s41612-026-01560-z","DOIUrl":"https://doi.org/10.1038/s41612-026-01560-z","url":null,"abstract":"Mountain regions are experiencing rapid climate change. However, temperature trends along elevation gradients remain poorly understood in the tropics, where long-term high-elevation air temperature (Tair) observations are sparse. Here, we examine elevation-dependent warming (EDW) on Mount Kilimanjaro by combining a unique long-term in situ Tair transect with Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST), surface and topographic datasets. Using machine learning, we map clear-sky Tair at four MODIS overpasses at 1 km resolution for 2004–2024. The models showed strong predictive performance, with RMSE values of 1.76 °C and 1.36 °C, and R2 values of 0.94 and 0.95 for daytime and nighttime models, respectively. Modelled Tair reveals strong diurnal contrasts: daytime Tair shows strong spatial variability affected by radiation and surface properties, while nighttime Tair varies more gradually with elevation. Trend analysis (2004–2019) identifies EDW under clear-sky conditions. Daytime trends become positive near the summit (+0.05 to +0.10 °C/decade), while strongest cooling occurs at 3000–3500 m (−0.13 to −0.14 °C/decade), coinciding with vegetation and land-cover change. Nighttime weak warming dominates most elevations (+0.04 to +0.15 °C/decade), while the summit remains near-neutral. These contrasts indicate that EDW on Kilimanjaro is shaped not only by elevation-dependent climate responses, but also by vegetation and land-cover changes that modify local surface-atmosphere coupling.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"6 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lei Chen, Zijian Zhu, Xiaoran Zhuang, Tianyuan Qi, Yuxuan Feng, Xiaohui Zhong, Hao Li
{"title":"Environment-conditioned deep learning for severe convection nowcasting","authors":"Lei Chen, Zijian Zhu, Xiaoran Zhuang, Tianyuan Qi, Yuxuan Feng, Xiaohui Zhong, Hao Li","doi":"10.1038/s41612-026-01558-7","DOIUrl":"https://doi.org/10.1038/s41612-026-01558-7","url":null,"abstract":"Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense convection remain challenging for radar-only nowcasting because pre-convective signals may be absent from recent radar observations, and strong echoes often decay rapidly in forecasts. Here we present FuXi-Nowcast, an environment-conditioned deep learning system that combines high-resolution observations with three-dimensional atmospheric forecasts to predict composite reflectivity, precipitation, wind gusts, and surface variables up to 12 h ahead. In April–July 2024 evaluations over East China, FuXi-Nowcast outperforms operational numerical weather prediction, persistence, and extrapolation baselines for reflectivity and precipitation. Case studies, diagnostics, and ablation experiments suggest that atmospheric moisture information and explicit preservation of strong convective signals contribute to forecasts of convective initiation and maintenance. These results show that environmental conditioning can mitigate important failure modes of radar-only nowcasting for high-impact convective weather.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"10 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A physics-guided integrated global to regional method for high-resolution weather forecasting","authors":"Jing Zhang, Yutong Dai, Yu Wang, Fang He, Pengbo Xu, Junping Yin","doi":"10.1038/s41612-026-01555-w","DOIUrl":"https://doi.org/10.1038/s41612-026-01555-w","url":null,"abstract":"High-resolution regional forecasts support disaster mitigation and sectoral decisions, yet numerical regional models remain costly. Here we introduce AERO-ODE, a physics-guided framework that combines global dynamical information, parameterization increments, and regional terrain constraints. From global initial conditions, AERO-ODE generates 72 h regional forecasts at hourly intervals and 3 km resolution in approximately 21 s, covering pressure-level Z, T, S, U and V and near-surface MSLP, U10, V10 and T2m, without separate global surface lateral-boundary inputs. At 48 h, the median reductions in RMSE across the 29 outputs are 29–32% and 28–33% compared with WRF-ARW and YingLong-WRF, respectively; all reductions relative to WRF-ARW are significant in both regions (p < 0.05). Median anomaly correlation is 0.04–0.05 higher than WRF-ARW, while extreme-event CSI and ETS improve for most variables. AERO-ODE generally shows lower errors than interpolated NeuralGCM, IFS and PanGu-Weather fields. These results demonstrate rapid, high-resolution regional forecasting from global dynamical information and regional constraints.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"298 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A tri‑basin matching index improves Indian Summer Monsoon Rainfall predictability","authors":"Shi-Yun Yu, Rong-Hua Zhang, Lei Fan","doi":"10.1038/s41612-026-01553-y","DOIUrl":"https://doi.org/10.1038/s41612-026-01553-y","url":null,"abstract":"Interannual variability of Indian Summer Monsoon Rainfall (ISMR) is vital for South Asian water resources and agriculture, but its prediction is challenged by non-stationary ocean–monsoon interactions. This study proposes a Matching Index (MI), defined as the pointwise product of normalized ISMR and sea surface temperature anomalies, combined with Empirical Orthogonal Function analysis (MI–EOF) to identify state-dependent ocean–monsoon coupling regimes. MI–EOF reveals three dominant regimes: ENSO intensity, distinguishing strong and weak ENSO states modulated by the Indian Ocean Dipole and tropical South Atlantic; ENSO lifecycle, highlighting phase-dependent effects of Indian Ocean Basin warming on ISMR; and ENSO spatial patterns, separating central- and eastern-Pacific ENSO influences on monsoon variability. Based on these physically interpretable regimes, a scenario-dependent reconstruction framework is developed to adaptively select predictors. The framework improves ISMR reconstruction correlation from 0.73 using conventional multivariate linear regression to 0.83, with stable performance throughout the record. Results demonstrate that ISMR variability is fundamentally state dependent and cannot be described by stationary linear relationships. The MI framework provides a physically based approach for diagnosing evolving air–sea coupling and improving monsoon prediction under climate change.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"36 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149087866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}