Low spatiotemporal resolution of global climate models (GCMs) outputs such as  CMIP6 models limits accurate detection of tropical cyclone (TC). Traditional statistical downscaling has difficulties in resolving non-linear relationships among different variables, while dynamical downscaling with regional high-resolution models is computational expensive and often distorts the results due to different dynamics framework with the GCMs. Here, we proposed a deep-learning based Multi-Variable Spatiotemporal Downscaling Generative Adversarial Network (MV-STD-GAN). It simultaneously spatiotemporally downscales five essential variables (sea level pressure, 300hPa/500hPa geopotential height, 10m zonal/meridional wind) closely related to TC detection. Trained on high- and low-resolution ERA datasets, it substantially improves the detection of observed TC and significantly outperforms traditional and other deep learning baselines, when subject to the same detection algorithm. It can also be successfully applied to low-resolution CMIP6 models, detecting TC activities very similar to the corresponding high-resolution models.

How to cite: Yuan, C. and Ye, Y.: A Deep Learning–Based Multi-Variable Spatiotemporal Downscaling Approach for High-Resolution Tropical Cyclone Detection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2397, https://doi.org/10.5194/egusphere-egu26-2397, 2026.

Machine learning (ML)-based models have recently demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather forecasting and subseasonal prediction. While prior efforts have assessed their ability to capture atmospheric dynamics at the synoptic scale, their performance across broader timescales and under out-of-distribution forcing remains insufficiently understood but essential criterion for establishing their credibility in Earth system science.

In this study, we design three idealized test cases to evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model that couples a dynamical core with ML-based physical parameterizations. The test casts span synoptic-scale phenomena, interannual variability, and out-of-distribution forcings via uniform warmings. We benchmark NeuralGCM against observations and conventional physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, and biases in the teleconnected wave train triggered by tropical SST anomalies. Furthermore, its simulated temperature responses near the tropopause and in the stratosphere under uniform warming simulations deviate from those in physics-based models, likely due to the biases in vertical temperature advection by the residual circulation.

Despite these limitations, NeuralGCM exhibits credible responses across all test cases and performs comparably to both observations and physics-based ESMs. These results suggest that hybrid models like NeuralGCM, which integrate dynamical cores with ML physics, offer a promising path toward the next generation of ML-based ESMs.

How to cite: Chen, Z., Leung, L. R., Zhou, W., Lu, J., Lubis, S. W., Liu, Y., Chang, J., Harrop, B. E., Wang, Y., Yang, M., Zhang, G., and Qian, Y.: Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22911, https://doi.org/10.5194/egusphere-egu26-22911, 2026.

Arctic sea ice has declined markedly over the past four decades, bringing new opportunities for commercial shipping and eco-tourism while elevating risks to shipping safety, which highlights the urgent demand for a seamless and skillful sea-ice prediction system. Current sea-ice prediction approaches fall into two main categories: statistical and dynamical models. The former (including machine learning) show promising performance in sea-ice concentration prediction but lack sufficient physical constraints due to their pure data-driven nature; the latter (including Earth system models) are physically grounded with strong consistency and multivariable coherence, yet suffer from high computational costs, imperfect parameterizations and accumulated forecast errors. To overcome these limitations, we propose a hybrid framework that integrates the complementary strengths of machine learning and dynamical models. Specifically, we develop an interactive ensemble prediction system, termed SUPER, which couples the machine learning model Ice-kNN with the Norwegian Climate Prediction Model (NorCPM) to enable recursive information exchange between the two models. Within SUPER, Ice-kNN and NorCPM run in parallel and exchange information at regular intervals (e.g., weekly), allowing mutual adjustment of their predictions. We conduct seasonal hindcasts for the period 2000–2023 and evaluate the daily sea-ice prediction skill of SUPER against that of the standalone models. Preliminary results indicate that the hybrid system substantially reduces errors in sea-ice concentration and sea-ice edge predictions for NorCPM, while yielding improvements for Ice-kNN. Further tuning and evaluation are ongoing, and updated results will be presented.

How to cite: Lin, Y., Wang, Y., Min, C., and Yang, Q.:  An Interactive Hybrid Framework Coupling Machine Learning and Dynamical Models for Arctic Sea Ice Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22955, https://doi.org/10.5194/egusphere-egu26-22955, 2026.

Mesoscale Convective Systems (MCSs) account for most of the rainfall over the Sahel and contribute substantially to extreme precipitation events (EPEs) that cause flooding in West Africa. However, the low density of ground-based observation networks limits the accurate monitoring and characterization of these events. Regional climate models, such as the Weather Research and Forecasting (WRF) model, help to compensate for this observational gap but still exhibit significant biases in simulating extreme precipitation.

This study aims to reduce these biases by integrating the WRF model into a Supermodelling framework based on the dynamic combination of multiple model configurations, designed to exploit their complementary strengths.

Two supermodels, SUPPERT-WRFA and SUPPERT-WRFB, are developed from three distinct WRF configurations that mainly differ in their convection schemes. SUPPERT-WRFA is trained using satellite-based IMERG precipitation, while SUPPERT-WRFB relies on atmospheric dynamical variables (U, V, and T) from ERA5. In both cases, the training strategy is based on a metric combining spatial correlation and root-mean-square error.

The performance of the supermodels is evaluated for the simulation of EPEs in West Africa through a case study of an extreme precipitation event that occurred on 5 September 2020 in Senegal. The results show a significant improvement in the representation of extreme precipitation compared to individual WRF configurations, highlighting the strong potential of Supermodelling to improve extreme event prediction in regions with sparse observational coverage.

How to cite: Dieng, A. L., Keenlyside, N., Koseki, S., and Schevenhoven, F.: WRF Supermodelling for Improved Simulation of Extreme Precipitation in West Africa, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22842, https://doi.org/10.5194/egusphere-egu26-22842, 2026.

The North Atlantic exhibits prominent multidecadal variability affecting climate impacts across Europe and the Arctic. Yet, separating internally generated variability from externally forced components remains challenging, especially when univariate indices are used and when models exhibit a low signal-to-noise ratio and biases in coupled feedbacks. Here we present a hybrid statistical–dynamical framework to identify and interpret a predictable coupled multidecadal mode linking the North Atlantic Oscillation (NAO), the Atlantic Meridional Overturning Circulation (AMOC), and Atlantic Multidecadal Variability (AMV), with implications for Arctic sea-ice variability.

We combine (i) long climate-model experiments (preindustrial control and transient forced integrations, including volcanic-only forcing) with (ii) subsampling/filtering using joint multivariate Singular Spectrum Analysis (MSSA) applied to a physically motivated set of fields spanning the stratosphere–troposphere–ocean system. Treating NAO/AMV as an inherently coupled multivariate process enables a clearer separation of signal from noise and isolates an oscillatory mode with a characteristic multidecadal timescale that emerges in unforced control conditions. External forcing primarily modulates the mode’s amplitude and apparent period rather than generating it: volcanic perturbations project efficiently onto ocean circulation and can intermittently excite the coupled state.

To interpret these results, we use a minimal conceptual model of a damped coupled NAO–AMV oscillator under periodic and stochastic forcing. The model demonstrates that multidecadal oscillations in unforced control simulations can be sustained by white-noise atmospheric variability, and clarifies how periodic forcing shape phase, period, and amplitude modulation.

How to cite: Omrani, N.-E. and Keenlyside, N.: A hybrid statistical–dynamical framework for a coupled multidecadal NAO–AMV oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22174, https://doi.org/10.5194/egusphere-egu26-22174, 2026.

While future emission pathways are the primary source of uncertainty in long-term global climate projections, internal climate variability dominates near-term uncertainty at the regional scale. Reducing this source of uncertainty is crucial, as it aligns with the planning horizons of stakeholders in climate-sensitive sectors. To address this challenge, decadal forecasts aim to reduce this uncertainty by initializing the climate model simulations from estimates of the observed state of the climate system, in order to phase the temporal evolution of the simulated and observed modes of climate variability. However, decadal forecasts are also subject to drift due to the initialisation shock arising from mismatch between biased models and assimilated observational estimates. In this study, we propose a novel decadal forecasting method based on a process-based constraint approach. This approach aims to align model internal variability with observations by selecting the member closest to the observed state—based on a given metric of interest—from a large ensemble of non-initialized simulations, generating a new ensemble from it, and repeating this process over time to produce decadal predictions. The added value of this approach is that it does not generate any drift. We demonstrate here its application in a case study predicting near-term surface temperatures over the North Atlantic, using observed subpolar gyre sea surface temperature as the basis for member selection.

How to cite: Bonnet, R., Boé, J., and Moine, M.-P.: Development of a new process-based constraint technique to provide decadal climate prediction over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18318, https://doi.org/10.5194/egusphere-egu26-18318, 2026.

The winter seasonal variability of the Northern Hemisphere Stratospheric Polar Vortex
(SPV) is characterized by extreme events known as Sudden Stratospheric Warmings
(SSWs). SSWs feature a rapid increase of the temperature of the stratospheric polar
cap and a reversal of the zonal winds, exerting a downward impact on the troposphere
that makes their prediction societally relevant. Yet, despite their importance, current
seasonal prediction systems (SPSs) show only limited skill in forecasting SPV
variability, and SSWs occurrence. This study quantifies to what extent existing
statistical--dynamical approaches, that were designed for the prediction of tropospheric
modes of variability, can improve SPV seasonal predictions. More specifically, we
investigate the efficacy of a teleconnection-based subsampling for the North Atlantic
Oscillation (NAO), which selects a subset of ensemble members based on their NAO
representation. We do this by combining the Copernicus Climate Change Service
(C3S) dynamical forecasts with statistical predictive models for the NAO index. This
approach is motivated by the established link between NAO and stratospheric
variability. Results show that the teleconnection-based subsampling technique
increases the prediction skill of winter SPV variability and of the number of SSW days
in a season, with improvements that are robust across all C3S individual models.
These improvements come together with a better representation of extra-tropical lower-
stratosphere wave activity during December and January (DJ). However, the study
demonstrates that only some of the NAO statistical predictors correlate with SPV
variability, and others show contrasting behavior. This has implications for future
development of hybrid forecast systems targeting both NAO and SPV.

How to cite: Gargiulo, F., Ruggieri, P., Famooss Paolini, L., and Di Sabatino, S.: A hybrid statistical-dynamical approach for seasonal prediction of the boreal winter stratosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12661, https://doi.org/10.5194/egusphere-egu26-12661, 2026.

Climate change is accelerating the frequency and intensity of heatwaves at an unprecedented rate, posing substantial threats to public health and socio-economic systems. To effectively mitigate future risks associated with extreme heat events, it is crucial to understand the decadal variability of heatwaves and to develop robust medium- to long-term adaptation strategies. However, owing to the complexity of internal climate variability, producing reliable, high-resolution decadal predictions of heatwaves over Taiwan and East Asia remains a major challenge.

This study focuses on the Pacific Meridional Mode (PMM), a key mode of climate variability that influences heatwave activity in East Asia through its modulation of large-scale atmospheric circulation over the North Pacific. By examining the spatial characteristics of heatwaves in Taiwan and their linkage to PMM variability, we develop statistical models that relate PMM to decadal variations in heatwave frequency across East Asia. To further enhance predictive skill and reduce model uncertainty, we also apply a Bayesian ensemble approach, which optimally combines information from multiple models based on their historical performance.

Our results demonstrate that incorporating PMM significantly improves the predictive skill of decadal heatwave forecasts, while the Bayesian ensemble method provides additional gains in forecast accuracy and robustness. These findings highlight the critical role of large-scale climate variability in governing extreme heat events and underscore the value of Bayesian ensemble techniques for advancing decadal climate prediction and supporting proactive climate risk management in Taiwan and East Asia.

How to cite: Tsai, C.-T., Tseng, W.-L., Wang, Y.-C., Shen, Y.-L., and Chu, P.-H.: Linking the Pacific Meridional Mode to Decadal Heatwave Prediction in Taiwan and East Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15590, https://doi.org/10.5194/egusphere-egu26-15590, 2026.

El Niño–Southern Oscillation (ENSO) variability arises from nonlinear interactions between the tropical ocean and atmosphere, combining deterministic recharge–discharge dynamics with episodic stochastic forcing. While conceptual models such as the Cane–Zebiak recharge oscillator capture the core physics of ENSO, forecast skill remains highly intermittent, with pronounced failures during periods of strong atmospheric variability. This study investigates the dynamical origins and predictability limits of ENSO by integrating a hierarchy of models, ranging from idealised oscillators to observationally forced and hybrid forecast frameworks.

Using ERA-20C equatorial zonal wind anomalies and Niño-3.4 sea surface temperature data, we identify and characterise Westerly Wind Bursts (WWBs) as state-dependent atmospheric perturbations that preferentially occur during warm ENSO phases. When imposed on the Cane–Zebiak oscillator, WWBs act as phase-dependent energy injections, modulating growth and decay without fundamentally altering the underlying oscillatory structure. However, these same perturbations substantially reduce the memory of the system, shortening the energy autocorrelation timescale from approximately 18–24 months to about 6 months during WWB-active periods.

A supervised forecast framework at an 8-month lead time reveals strong regime dependence in predictability. While including atmospheric forcing improves mean forecast skill, performance collapses during WWB months, with large, biased, and phase-dependent errors. Linear and machine-learning residual correction models fail under cross-validation, indicating that WWB-induced errors are not deterministically predictable on an event-by-event basis. Instead, forecast error variance exhibits robust phase dependence, enabling the identification of distinct uncertainty regimes.

Building on this structure, we introduce a hybrid, regime-aware forecasting strategy that applies deterministic prediction only during low-uncertainty regimes and adopts conservative alternatives during high-uncertainty WWB conditions. This approach reduces catastrophic forecast errors and improves reliability without overfitting. Overall, the results demonstrate that ENSO is a conditionally predictable system, where atmospheric forcing not only modulates amplitude but imposes intrinsic limits on deterministic forecast skill. These findings argue for forecast systems that explicitly represent uncertainty and regime transitions rather than relying solely on universal deterministic correction.

How to cite: Pigelet, A.: Understanding Atmospheric Oscillations and Climate Change Using a Hierarchy of Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13645, https://doi.org/10.5194/egusphere-egu26-13645, 2026.

Introduction: Observational and modeling studies have examined the
interactions between El Niño-Southern Oscillation (ENSO) and the equatorial
Atlantic variability as incorporated into the classical charge-recharge oscillator
model of ENSO. These studies included the role of the Atlantic in the
predictability of ENSO but assumed stationarity in the relationships, i.e., that
models’ coefficients do not change over time. A recent work by the authors has
challenged the stationarity assumption in the ENSO framework but without
considering the equatorial Atlantic influence on ENSO.
Methods: The present paper addresses the changing relationship between
ENSO and the Atlantic El Niño using an extended version of the recharge
oscillator model. The classical two-variable model of ENSO is extended by
adding a linear coupling on the SST anomalies in the equatorial Atlantic. The
model’s coefficients are computed for different periods. This calculation is
done using two methods to fit the model to the data: (1) the traditional method
(ReOsc), and (2) a novel method (ReOsc+) based on fitting the Fisher’s Z
transform of the auto and cross-correlation functions.
Results: We show that, during the 20th century, the characteristic damping rate
of the SST and thermocline depth anomalies in the Pacific have decreased in
time by a factor of 2 and 3, respectively. Moreover, the damping time of the
ENSO fluctuations has doubled from 10 to 20 months, and the oscillation
period of ENSO has decreased from 60-70 months before the 1960s to 50
months afterward. These two changes have contributed to enhancing ENSO
amplitude. The results also show that correlations between ENSO and the
Atlantic SST strengthened after the 70s and the way in which the impact of the
equatorial Atlantic is added to the internal ENSO variability.
Conclusions: The remote effects of the equatorial Atlantic on ENSO must be
considered in studies of ENSO dynamics and predictability during specific
time-periods. Our results provide further insight into the evolution of the ENSO
dynamics and its coupling to the equatorial Atlantic, as well as an improved tool
to study the coupling of climatic and ecological variables.

How to cite: Cao-García, F. J., Crespo-Miguel, R., Polo, I., Mechoso, C. R., and Rodriguez-Fonseca, B.: ENSO coupling to the equatorialAtlantic: Analysis with an improved recharge oscillator model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21543, https://doi.org/10.5194/egusphere-egu26-21543, 2026.