A global 0.05° dataset of daily latent heat flux and resistances (2001–2020) derived via the Energy–Resistance–Coupling framework

Reliable estimation of LE depends strongly on effective aerodynamic resistance (rae​) and surface resistance (rs​), which regulate turbulent transport and surface water-vapour transfer. However, most existing global products provide only final flux estimates and rarely include long-term, spatially explicit resistance-related variables.This dataset provides global daily estimates of LE, rae​, rs​​, the atmosphere–surface decoupling coefficient (Ω), and the maximum surface relative humidity parameter (hs,max⁡​) at a spatial resolution of 0.05° for the period 2001–2020.

The dataset was generated using an Energy–Resistance–Coupling (ERC) iterative framework driven by multi-source remote-sensing data, meteorological reanalysis, and flux-tower observations. The framework incorporates an aerodynamic temperature (T0​) calibration scheme, an XGBoost-based dynamic constraint on hs,max⁡​​, and a Newton–Raphson solver to obtain physically constrained estimates of the resistance network and latent heat flux.The dataset was evaluated against observations from 244 flux sites.