Wet‑year productivity pulses dominate asymmetric vegetation responses to precipitation variability on the Loess Plateau

Understanding vegetation productivity (VP) responses to precipitation variability on China’s Loess Plateau is critical for managing fragile transitional dryland ecosystems, yet these responses and the underlying drivers remain poorly understood. Combining long-term ground and remote sensing observations with an explainable extreme gradient boosting (XGBoost) machine‑learning framework, we found that VP, measured as gross and net primary productivity (GPP and NPP), responded asymmetrically to precipitation anomalies during 2001–2020, with wet-year gains generally exceeding dry-year losses. A five-year moving-window analysis revealed shifting temporal patterns of asymmetry, where wet-year gains peaked during periods of increased precipitation, while drought stress dominated during prolonged dry spells. Asymmetric responses varied across precipitation gradients and biomes, with the magnitude of asymmetry following a unimodal pattern along the precipitation gradient, peaking in semi-arid regions (300–500 mm yr−1). Grasslands exhibited the strongest positive asymmetry due to substantial wet-year gains, while forests showed greater stability and lower asymmetry, reflecting their buffering capacity. Rainfed croplands experienced high sensitivity to precipitation variability but balanced wet- and dry-year responses, resulting in low asymmetry, whereas irrigated croplands displayed reduced sensitivity and higher asymmetry due to irrigation. At the regional scale, VP asymmetry was primarily driven by low-precipitation extremes (31.50%) and interannual precipitation variability (22.86%). However, these drivers varied significantly across biomes: mean precipitation for forests and irrigated croplands, extreme wet‑year precipitation for grasslands, and variability coupled with dryness for rainfed croplands. These findings provide critical insights to convert episodic wet‑year gains into more sustained productivity and improve the resilience and sustainability of dryland ecosystems in the face of increasing climate variability, particularly for predicting regional carbon sink dynamics and formulating adaptive management strategies.