SoilFutures-BR: Bias-Corrected Soil Temperature Projections for Brazil

Soil temperature is a fundamental variable for the Earth system, yet it remains highly sensitive to climate change, particularly in tropical regions such as Brazil. Despite its importance, no bias-corrected soil temperature dataset based on the latest CMIP6 projections is currently available for the country. Climate projections are model-dependent and often exhibit systematic biases, which makes bias correction an essential step for subsequent applications. To address this gap, we present STEM-BR: Soil Temperature Under Climate Change for Brazil, a new gridded dataset of historical (1950–2014) and future (2015–2100) soil temperature derived from 15 CMIP6 General Circulation Models (GCMs) under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The dataset was generated through systematic regridding, bias correction, and statistical refinement, and provides monthly series at 0.25° × 0.25° resolution, including both raw and bias-corrected outputs. We applied the Quantile Delta Mapping (QDM) approach to correct biases in monthly time series of soil temperature at three depths (0.07, 0.28, and 1.00 m).