Long-term spatiotemporal evolution, relative hotspot persistence, and predictive attribution of PM_2.5 across China

China's PM_2.5 pollution has declined markedly since 2013, but whether its pace, spatial hierarchy, and predictive structure changed concurrently remains unclear. We combined the 1-km ChinaHigh PM_2.5 product for 2000-2024 with homogenized ground observations at 1269 fixed sites and analysed segmented trends, meteorological normalization evaluated at randomly held-out sites, fixed-population exposure, repeated spatial-block validation of global XGBoost and a block-centred geographically weighted variant (GeoXGBoost), and cross-fitted SHAP effects. The national mean fell from 48.20 μgm^-3 in 2013 to 24.83 in 2024, while the Theil-Sen decline slowed from -3.489 μgm^-3yr^-1 during 2014-2018 to -1.030 during 2019-2024. Meteorological normalization preserved this ordering. Although 75.2-88.2% of early hotspot area remained in the corresponding late-period upper tail, fixed-2020-population exposure decreased by 19.57 μgm^-3 between 2000-2004 and 2020-2024. Across 40 held-out folds from eight spatial partitions, mean R² was 0.208 for XGBoost and 0.175 for GeoXGBoost; the partition-level paired difference was 0.033 (bootstrap 95% confidence interval, 0.015-0.048). Forward-year R² ranged from 0.794 to 0.842 with the year index and from -0.228 to 0.275 without it. Low spatial scores may reflect clustered monitoring, environmental differences among held-out regions, and missing spatially resolved processes. Spatial out-of-fold SHAP rankings nevertheless replicated for ground observations (Spearman ρ = 0.983), and the strongest interactions linked temperature with humidity and latitude. These findings separate persistent spatial ranking from declining absolute exposure and identify geographic, temporal, and response-level stress tests as safeguards for generalizing predictive PM_2.5 attribution.