{"url":"/dataset/rainnet","name":"RainNet","full_name":null,"description_markdown":"**RainNet** is a real (non-simuated) large-scale spatial precipitation downscaling dataset that contains 62,424 pairs of low-resolution and high-resolution precipitation maps for 17 years. Contrary to simulated data, this real dataset covers various types of real meteorological phenomena (e.g., Hurricane, Squall, etc.), and shows the physical characters - Temporal Misalignment, Temporal Sparse and Fluid Properties - that challenge the downscaling algorithms.\n\nSource: [https://github.com/neuralchen/RainNet](https://github.com/neuralchen/RainNet)\nImage Source: [https://github.com/neuralchen/RainNet](https://github.com/neuralchen/RainNet)","description_withheld":null,"homepage":"https://github.com/neuralchen/RainNet","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/rainnet-a-large-scale-dataset-for-spatial","title":"RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling","first_author":"Xuanhong Chen","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["RainNet"],"data_loaders":[{"repo":"https://github.com/neuralchen/RainNet","url":"https://github.com/neuralchen/RainNet","frameworks":[]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}