Explore, analyze, and export historical rainfall readings across any region and any time period — from 1950 to today.
Climate researchers, urban planners, agriculture firms, and insurers all need reliable long-term rainfall records. Scattered CSVs and outdated portals make analysis painful. RainStats puts 70+ years of curated data behind a clean, queryable interface.
Government portals, NOAA exports, and local CSVs in different formats and resolutions — stitching them together takes weeks.
Static files give you raw numbers, not insights. Spotting multi-decade trends requires building your own tooling from scratch.
Missing readings, station relocations, and unit inconsistencies corrupt long-term comparisons without careful curation.
From raw station readings to publication-ready charts — all in one platform.
Zoom, pan, and filter across any date range. Compare multiple stations on the same chart with a single click.
Find any of our 12,000+ stations by city, region, coordinates, or NOAA ID. Radius-based cluster queries included.
Automatic highlighting of extreme events — record highs, droughts, and multi-year deviations from the historical baseline.
Download your filtered dataset as CSV, JSON, or Excel. Chart exports as high-res PNG or SVG for reports and publications.
Instantly switch between daily, monthly, seasonal, and annual aggregations. Resample on the fly with percentile bands.
Integrate rainfall data directly into your models, dashboards, or applications. Full OpenAPI spec, SDKs for Python and R.
Monthly Average Rainfall
Seattle, WA · 2000–2024
Annual Total vs. Baseline
Los Angeles, CA · 1980–2024
Use the map picker or search by name, coordinates, or NOAA station ID. Multi-select for cross-regional comparisons.
Pick any range from 1952 to present. Choose daily, monthly, seasonal, or annual granularity with one click.
Interact with auto-generated charts, spot anomalies, compare to the 30-year baseline, and export your dataset or visualization in seconds.
"We replaced a 3-week data-wrangling pipeline with a 20-minute RainStats export. The anomaly detection saved us from a major error in our drought frequency study."
Dr. Sarah Donnelly
Climate Scientist, MIT
"Our agricultural risk models needed 40+ years of station data across 6 countries. RainStats delivered via API in minutes. The Python SDK is clean and well-documented."
Marcus Krüger
Lead Data Engineer, AgroRisk GmbH
"As an urban planner, I needed 50-year flood-frequency curves. RainStats had the data, the tools to compute return periods, and export to our GIS workflow."
Tomoko Patel
Urban Planner, City of Portland
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