arXiv subjects

Neelesh Rampal

Publications and source records attributed to Neelesh Rampal.

5 recordsLinked to original sources

arXiv2026

CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling -Experiment Design and Overview

Machine learning (ML) has emerged as a cost-effective approach to complement dynamical downscaling for producing high-resolution regional climate projections. However, the absence of standardised training and evaluation protocols, applied consistently across multiple domains, continues to hinder meaningful model intercomparison. We introduce CORDEX-ML-Bench, a benchmark aligned with CORDEX, which constitutes the first phase of a community initiative to advance data-driven downscaling toward operational readiness, and complement future dynamical downscaling efforts under CMIP7. The framework targets downscaled daily maximum temperature and precipitation to ~10 km resolution (20x increase) across three pilot regions; European Alps, New Zealand, and Southern Africa. Using a perfect-model experimental design, we evaluate 40 ML configurations developed independently, spanning traditional ML, convolutional U-Nets, vision transformers, graph neural networks, and generative models based on diffusion, flow matching, and generative adversarial networks. Models are trained under two experimental periods, an empirical-statistical downscaling pseudo-reality (historical period only) and Emulator (historical and future periods) -and are evaluated against a core set of metrics developed specifically for assessing downscaling skill. Generative models consistently outperform deterministic approaches for precipitation, better capturing fine-scale variability and extremes. For temperature, the generative advantage narrows and deterministic architectures remain competitive. Models trained solely on the historical period systematically underestimate future climate-change signals while those additionally trained on a future period perform better. These findings raise concerns about historically trained models widely used in an operational setting, underscoring the need for rigorous extrapolation testing.

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arXiv2025

An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales

Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth fine-scale structures and underestimate extremes. While stochastic generative models show promise, few studies have compared their skill under both present-day and future climates. This study compares Generative Adversarial Networks (GANs), flow matching and diffusion models across multiple configurations for downscaling daily precipitation from a regional climate model (RCM) over New Zealand. Model skill is assessed across spatial structure, distributional metrics, climatological means, extremes, ensemble calibration, and climate change signals. Unlike GANs, diffusion and flow matching models generate predictions through many sequential steps. Here we show that using higher-order differential equation solvers, the number of steps required can be reduced with only a minor reduction in skill, heavily reducing the computational burden for downscaling large ensembles, which may have otherwise prevented their use in operational settings. Overall, GANs, flow matching and diffusion perform competitively across most metrics, except that diffusion and flow matching produce higher-fidelity predictions and better-calibrated ensembles compared to GANs -which are under-dispersive. Most approaches capture mean precipitation signals reasonably well, but underestimate end-of-century climate change signals of extreme precipitation, despite being trained on RCM simulations spanning the future period. Only one GAN and one flow matching configuration can reproduce this change signal reliably. These results highlight the importance of evaluating model performance across a comprehensive set of metrics, and that neither visual realism nor good skill on standard metrics guarantee skill in predicting climate change signals.

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arXiv2025

Generative AI-Downscaling of Large Ensembles Project Unprecedented Future Droughts

Understanding how droughts may change in the future is essential for anticipating and mitigating their adverse impacts. However, robust climate projections require large amounts of high-resolution climate simulations, particularly for assessing extreme events. Here, we use a novel dataset, multiple large-ensembles of Global Climate Models (GCMs), downscaled to 12km using generative AI, to quantify the future risk of meteorological drought across New Zealand. The ensembles consists of 20 GCMs, including two single-model initial condition large ensembles. The AI is trained to emulate a physics-based regional climate model (RCM) used in dynamically downscaling, and adds a similar amount of value as the RCM across precipitation and drought metrics. Marked increases in precipitation variability are found across all ensembles, alongside highly uncertain changes in mean precipitation. Future projections show droughts will become more intense across the majority of the country, however, internal variability and model uncertainty obscure future changes in drought durations and frequency across large portions of the country. This uncertainty is understated using a smaller number of dynamically-downscaled simulations. We find evidence that extreme droughts up to twice as long as those found in smaller ensembles, could occur across the entirety of the country in the current climate, highlighting the value of long-duration downscaled simulations to sample rare events. These extremely long droughts increase in length in many locations under a high emissions SSP3-7.0 scenario giving rise to events around 30 months long in some locations.

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arXiv2025

Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes

The computational cost of dynamical downscaling limits ensemble sizes in regional downscaling efforts. We present a newly developed generative-AI approach to greatly expand the scope of such downscaling, enabling fine-scale future changes to be characterised including rare extremes that cannot be addressed by traditional approaches. We test this approach for New Zealand, where strong regional effects are anticipated. At fine scales, the forced (predictable) component of precipitation and temperature extremes for future periods (2080--2099) is spatially smoother than changes in individual simulations, and locally smaller. Future changes in rarer (10-year and 20-year) precipitation extremes are more severe and have larger internal variability spread than annual extremes. Internal variability spread is larger at fine scales that at the coarser scales simulated in climate models. Unpredictability from internal variability dominates model uncertainty and, for precipitation, its variance increases with warming, exceeding the variance across emission scenarios by fourfold for annual and tenfold for decadal extremes. These results indicate that fine-scale changes in future precipitation are less predictable than widely assumed and require much larger ensembles to assess reliably than changes at coarser scales.

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arXiv2024

On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates

While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM). Both approaches emulate future changes in annual mean precipitation well, even when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end-of-century increase with future warming (~5.8%/{\deg}C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming-driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data.

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