Interpretable Ensemble Machine Learning for Statistical Emulation of Summer PM2.5 Change in GEOS-CF over the Arabian Peninsula: How Far Does Spatially Blocked Skill Transfer?

Fine particulate matter (PM2.5) over the Arabian Peninsula is poorly monitored at the surface. This study emulates the relative change in summer (June–August) PM2.5 between 2020 and 2025 in the NASA GEOS-CF composition system with interpretable ensemble machine learning, using thirteen predictors from Sentinel-5P TROPOMI, ERA5-Land, CHIRPS, and MODIS Aqua on the native 0.25° grid (3933 cells), and tests how far the resulting skill transfers in space. Under 10 × 10 spatial blocking, tree-based ensembles reached cross-validated R2 = 0.82–0.84, against 0.55 for linear regression. An identically tuned Random Forest using only latitude and longitude performed better (R2 = 0.91) and remained better under larger blocks, exclusion buffers, and four of five held-out sectors; combined with the coordinates, the predictors added only modest skill. The predictor model lost all skill once training cells within 300 km were excluded, failed on an unseen eastern sector containing the single contiguous cluster of changes above +40% (r = 0.11), and did not transfer to other summers, and interpolation of the reference filled simulated gaps more accurately than every emulator. The leading predictor, the total-column O3 change, is a latitudinal gradient (r = 0.94). Split-conformal intervals under a separated spatial design covered 82–98% of test cells at the 90% nominal level but 60% in the eastern sector. The spatially blocked skill of such emulators is therefore interpolative and should be reported together with transfer diagnostics.