Bias adjustment is standard preprocessing for climate model output used in crop-impact and damage-assessment work. Most validation studies still emphasize mean temperature, even though agricultural applications often depend on nonlinear heat metrics built from daily extremes. We evaluate how bias adjustment propagates into Extreme Degree Day (EDD) metrics, which crop-yield and damage functions use to summarize heat stress above specified temperature cutoffs. Using ERA5 reanalysis as reference, we compare EDD from raw and bias-adjusted CMIP6 models (GFDL-ESM4, MPI-ESM1-2-HR, and EC-Earth3) across European ADM1 regions for 1994–2014. We compute EDD at seven thresholds (0, 4, 8, 12, 28, 30, and 32 °C), aggregate it with harvested-area weights for seven crops under rainfed and irrigated management, and sum it over crop-specific phenological windows. Bias adjustment reduces mean absolute error at low and moderate thresholds (EDD $ \le $ 12 °C) by 37–86 °C$ \cdot $days at EDD-0 and 29–64 °C$ \cdot $days at EDD-8. At extreme thresholds (EDD $ \ge $ 28 °C), $ \Delta $Skill is small on average (1–4 °C$ \cdot $days at EDD-28), and bias adjustment increases error in about 40% of evaluated combinations. The split between helpful and unhelpful bias adjustment follows the EDD threshold more closely than model identity, which has implications for impact studies that treat bias-adjusted data as uniformly preferable.

Multi-model ensembles are widely used in climate science, yet Coupled Model Intercomparison Project Phase 6 (CMIP6) models are neither independent nor equally skillful. We present a framework for interpretable ensemble learning that improves prediction while making model contributions explicit. Using monthly CMIP6 near-surface temperature and precipitation (1948–2014) against ERA5, we compare arithmetic multi-model ensemble (AMME) averaging with Ridge regression, random forest, and a continuous gating mixture that adaptively interpolates between them. On held-out test years (2006–2014), gating achieves the best temperature performance and improves precipitation distributional behavior relative to both AMME and individual learners. We trace these gains back to individual models and space: dominant-cluster and dominant-model maps show that gating reallocates trust in regionally coherent patterns rather than collapsing to a single best model, while entropy-based diagnostics quantify where trust is concentrated versus distributed. These analyses provide a practical pathway for transparent ensemble learning that supports both predictive skill and scientific accountability.

The European electricity power grid is transitioning toward renewable energy sources, characterized by an increasing share of offshoreand onshore wind and solar power. However, the weather dependency of these energy sources poses a challenge to grid stability, with so-called Dunkelflaute events—periods of low wind and solar power generation—being of particular concern due to their potential to cause electricity supply shortages. In this study, we investigate the impact of these events on the German electricity production in the years and decades to come. For this purpose, we adapt a recently developed generative deep learning framework to downscale climate simulations from the CMIP6 ensemble. We first compare their statistics to the historical record taken from ERA5 data. Next, we use these downscaled simulations to assess plausible future occurrences of Dunkelflaute events in Germany under the optimistic low (SSP2–4.5) and high (SSP5–8.5) emission scenarios. Our analysis indicates that both the frequency and duration of Dunkelflaute events in Germany in the ensemble mean are projected to remain largely unchanged compared to the historical period. This suggests that, under the considered climate scenarios, the associated risk is expected to remain stable throughout the century.

Simulated data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) has been very important for climate science research, as they can provide wide spatio-temporal coverage to address data deficiencies in both present and future scenarios. However, these physics-based models require a huge amount of high-performance computing (HPC) resources. As an alternative approach, researchers are exploring if such simulated data can be generated by Generative Machine Learning models. In this work, we develop a model based on Pix2Pix conditional Generative Adversarial Network (cGAN), which can generate high-resolution spatial maps of global sea surface temperature (SST) using comparatively less computing power and time. We have shown that the maps generated by these models have similar statistical characteristics as the CMIP6 model simulations. Notably, we trained and validated our cGAN model on completely distinct time periods across all ensemble members of the EC-Earth3-CC and CMCC-CM2-SR5 CMIP6 models, demonstrating satisfactory results and confirming the generalizability of our proposed model.

This paper derives statistical models for predicting wintertime subseasonal temperature over the western US. The statistical models are trained on two separate datasets, namely observations and dynamical model simulations, and are based on least absolute shrinkage and selection operator (lasso). Surprisingly, statistical models trained on dynamical model simulations can predict observations better than observation-trained models. One reason for this is that simulations involve orders of magnitude more data than observational datasets.

Simulations of future climate contain variability arising from a number of sources, including internal stochasticity and external forcings. However, to the best of our abilities climate models and the true observed climate depend on the same underlying physical processes. In this paper, we simultaneously study the outputs of multiple climate simulation models and observed data, and we seek to leverage their mean structure as well as interdependencies that may reflect the climate’s response to shared forcings. Bayesian modeling provides a fruitful ground for the nuanced combination of multiple climate simulations. We introduce one such approach whereby a Gaussian process is used to represent a mean function common to all simulated and observed climates. Dependent random effects encode possible information contained within and between the plurality of climate model outputs and observed climate data. We propose an empirical Bayes approach to analyze such models in a computationally efficient way. This methodology is amenable to the CMIP6 model ensemble, and we demonstrate its efficacy at forecasting global average near-surface air temperature. Results suggest that this model and the extensions it engenders may provide value to climate prediction and uncertainty quantification.