mlsanityData to 14 Oct 2026

ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method

arXiv 2504.07394 ↗First posted 10 Apr 2025cs.LG cs.AIcited by 4

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Abstract (arXiv)

Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc. Recently, much research attention has been paid to the climate benchmarks. In addition to the most common task of weather forecasting, several pioneering benchmark works are proposed for extending the modality, such as domain-specific applications like tropical cyclone intensity prediction and flash flood damage estimation, or climate statement and confidence level in the format of natural language. To further motivate the artificial general intelligence development for climate science, in this paper, we first contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns (1) the time series climate data from ERA5, (2) extreme weather events data from NOAA, and (3) satellite image data from NASA HLS based on a unified spatial-temporal granularity. Second, 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 in the proposed ClimateBench-M. The data and code of ClimateBench-M are publicly available at https://github.com/iDEA-iSAIL-Lab-UIUC/ClimateBench-M.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 97 references that are in the map. First posted on arXiv 10 Apr 2025.

Builds on

37 of 97 references.

References · 37

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25 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14
  2. 2025Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecastingcited by 12
  3. 2026OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detectioncited by 2
  4. 2026The Rise of AI in Weather and Climate Information and its Impact on Global Inequalitycited by 0