Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling

• URL: http://arxiv.org/abs/2606.07898v1
• Date: Fri, 05 Jun 2026 23:16:38 GMT
• Title: Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling
• Abstract summary: High-resolution regional climate simulations provide critical information for climate impacts assessments.<n>Findings provide practical guidance for regional climate downscaling and large-ensemble projection.
• Score: 6.383771493909507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators. A key challenge is determining how limited high-resolution simulations should be distributed across a changing climate trajectory to capture both forced climate response and internal variability. Using the CESM2 Large Ensemble over the western United States, we compare three training-year selection strategies under fixed data budgets: a contiguous block of historical years, years drawn from both the beginning and end of the simulation period, and years distributed throughout the full climate trajectory. Including both historical and future years consistently outperforms training on historical years alone, demonstrating the importance of exposing downscaling models to climate states outside the historical record and highlighting limitations of stationarity assumptions common in statistical downscaling. Training on years distributed throughout the full climate trajectory performs best overall, indicating that broad sampling of internal variability provides additional information beyond exposure to the forced climate response alone. Models trained on temporally distributed subsets more successfully reproduce variability in unseen ensemble members while retaining strong performance across a wide range of climate diagnostics. Even when trained on only one-tenth of the available high-resolution years, temporally distributed models remain highly competitive with full-data training. These results suggest that, under fixed computational budgets, broad sampling of climate states is more valuable than temporal continuity when allocating scarce high-resolution simulations. The findings provide practical guidance for regional climate downscaling and large-ensemble projection workflows.

Related papers

• Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift [4.204964414976613]
  Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections.<n>Models trained on historical data are commonly applied to future projections whose distributions may differ substantially from the training period.<n>We propose a temporal domain-adaptive downscaling framework that combines supervised HR reconstruction on historical data with domain alignment between historical and future climate distributions.
  arXiv  Detail & Related papers  (2026-07-06T21:12:48Z)
• Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts [2.4329932351753114]
  "No-analog" future climate states are regimes where external forcing drives the system into conditions outside the empirical range of the historical training data.<n>We benchmark the robustness of three state-of-the-art architectures: U-Net, ConvLSTM, and the ClimaX foundation model specifically restricted to a historical-only training regime.
  arXiv  Detail & Related papers  (2026-03-24T10:29:54Z)
• Toward generative machine learning for boosting ensembles of climate simulations [0.0]
  We develop a conditional Variational Autoencoder (cVAE) trained on a limited sample of climate simulations to generate arbitrary large ensembles.<n>We show that the cVAE model learns the underlying distribution of the data and generates physically consistent samples that reproduce realistic low and high moment statistics.
  arXiv  Detail & Related papers  (2026-02-06T00:54:19Z)
• ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method [61.76389719956301]
  We contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns time series climate data from ERA5, extreme weather events data from NOAA, and satellite image data from NASA.<n>Under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks.
  arXiv  Detail & Related papers  (2025-04-10T02:22:23Z)
• A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation [18.881422165965017]
  We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics.<n>We demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output.
  arXiv  Detail & Related papers  (2024-12-19T19:47:35Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks [2.937141232326068]
  We train two "loosely conditioned" Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model. GANs are trained to producetemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense.
  arXiv  Detail & Related papers  (2021-04-29T02:10:08Z)
• DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
  Earth system models (ESMs) are often used to generate future projections of climate change scenarios. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
  arXiv  Detail & Related papers  (2020-11-23T20:13:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.