Yi Xu
Abstract
Satellite precipitation products such as IMERG exhibit biases that vary with terrain, season, and precipitation regime, leaving the applicability boundaries of machine learning correction unclear. This study proposes the Terrain-Moisture-Intensity (TMI) framework, centered on mechanism purity, extending the correction problem from purely algorithmic optimization to physical consistency diagnosis. A proof-of-concept study in Hunan Province employs IMERG V07, SRTM DEM, and ERA5 variables (tcwv, u10, v10). Ablation results indicate that, under the conditions of this study, terrain-moisture relationships are predominantly additive: RF-Full yields merely +0.001 R^2 gain over LR-Full, while bias rises to 1.282 mm d^-1; MAE decreases by approximately 14%, reflecting a trade-off between tail-fitting improvement and mean shift. SHAP diagnostics identify three categories of boundaries. Spatially, Central Hunan exhibits significant degradation (R^2=0.133) despite strong variable activation, consistent with mechanism fragmentation induced by mixed terrain. Temporally, u10 undergoes directional reversal between summer and spring (+0.096 to -0.156), presenting "silent failure." Extreme precipitation (>=50 mm d^-1) approximates a mechanism saturation frontier rather than isolated out-of-distribution samples, with DEM showing the largest relative amplification in SHAP disorder (+150%). The results demonstrate that machine learning correction performance is primarily constrained by mechanism purity. A pre-registered cross-regional test (Hunan, Guangxi, Guangdong) confirms this screening capability out of sample: a priori coherence proxies predict correction efficiency with a mean absolute error of 2.6 percentage points, while the transfer-versus-retraining contrast separates mechanism mismatch (coastal Guangdong) from portability (Guangxi), establishing the framework as a validated applicability screen.