Bayesian hierarchical downscaling of station observations with learned covariance kernels: an artificial intelligence evaluation of uncertainty propagation in national climate normals
National climate normals are typically reported as single numbers, yet they inherit uncertainty from sparse station networks, interpolation choices and covariance assumptions. The framework is applied to NASA POWER monthly reanalysis air temperature at 2 m for Lagos, Nigeria (6.52°N, 3.38°E) over 2000–2023, comprising...