CONTENTS
Bias correction is a process used to adjust climate model outputs so they more closely reflect observed real-world data. It corrects for known discrepancies and helps make climate projections more accurate and relevant to specific locations.
Example in context
You're helping a client assess wildfire risk at a site in Portugal. Without bias correction, the climate model overestimates historical temperature trends. Some climate risk intelligence platforms apply correction using satellite and station data producing a more accurate, localised view of past and future conditions.
Why it matters
All climate models have biases but relying on raw outputs can lead to misleading results. Bias correction improves accuracy by aligning projections with observed data, ensuring risk assessments are realistic. EarthScan’s process uses 100,000+ stations and peer-reviewed datasets to achieve this.
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