Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC.

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.

Snow cover over Tibetan Plateau (TP) is not only a key land forcing for the regional and global climate but also an important water resource for surround regions. However, state-of-the-art climate models still exhibit substantial biases in simulating winter snow cover over the TP, which constitutes one of the major sources of uncertainty in climate prediction. Using satellite-based snow cover datasets, this study reveals that the Community Land Model version 5 (CLM5) systematically overestimates the winter snow cover fraction (SCF) over the TP. This bias mainly arises because the original SCF parameterization scheme neglects the spatially varying probability distribution of snowfall accumulation and underestimates snow depletion over barren land during the melting period. By accounting for the effects of non-growing-season low vegetation (i.e., withered grass stems) and topographic relief, we parameterize the snow accumulation probability factor (k_accum) instead of prescribing it as a constant. In addition, a revised factor is introduced to modify the snow depletion curve shape parameter (N_melt), thereby optimizing the SCF parameterization scheme. Preliminary validation indicates that the optimized scheme substantially reduces positive winter SCF biases over barren land and grassland by 34 %~88 %, and improves surface albedo simulations, thereby alleviating cold surface temperature biases by approximately 1~2 °C in snow-affected regions.

Snow cover over the Tibetan Plateau (TP) is not only a key land forcing for the regional and global climate but also an important water resource for surround regions. However, state-of-the-art climate models still exhibit substantial biases in simulating winter snow cover over the TP, which constitutes one of the major sources of uncertainty in climate prediction. Using satellite-based snow cover datasets, this study reveals that the Community Land Model version 5 (CLM5) systematically overestimates the winter snow cover fraction (SCF) over the TP. This bias mainly arises because the original SCF parameterization scheme neglects the spatially varying probability distribution of snowfall accumulation and underestimates snow depletion over barren land during the melting period. By accounting for the effects of non-growing-season low vegetation (i.e., withered grass stems) and topographic relief, we parameterize the snow accumulation probability factor (kaccum) instead of prescribing it as a constant. In addition, a revised factor is introduced to modify the snow depletion curve shape parameter (Nmelt), thereby optimizing the SCF parameterization scheme. Preliminary validation indicates that the optimized scheme substantially reduces positive winter SCF biases over the entire TP by 63 %, and improves surface albedo simulations, thereby alleviating cold surface temperature biases by approximately 1–2 °C in snow-affected regions.

Precipitation events in dryland environments generate sharp but uneven adjustments in surface and atmospheric conditions. While the atmosphere recovers rapidly from rainfall-induced cooling, the soil retains a substantial portion of the cooling anomaly, creating a land-surface memory. Using multi-year, multi-layer observations from five stations in Ningxia, China, and ERA5 reanalysis, this study investigates how this soil thermal memory timescale (τ_rec) modulates the timing of recycled moisture return. Analysis of 112 events reveals a consistent "cold-humid pulse" with rapid atmospheric recovery but slow soil recovery, whose persistence we quantify as τ_rec (20–80 hours) using an exponential-decay framework. ERA5 diagnostics show the recycled moisture signal peaks 20–40 hours after rainfall, defining a recycling lag (τ_RR). Event-wise analysis of ten long-duration events reveals a systematic positive correlation (R ≈ 0.57) between τ_rec and τ_RR. Longer soil memory consistently predicts a more delayed recycling peak. We show this relationship is mediated by enhanced moisture-heat feedback (H), where persistent cold soils slow boundary-layer recovery and postpone the reactivation of evaporation. These results identify soil thermal memory as an active regulator; the timing of recycled moisture is not solely an atmospheric process but is partially land-controlled. This work establishes a novel "coupling-memory-recycling" pathway, providing a new mechanism for understanding and modeling dryland precipitation dynamics.

Precipitation events in dryland environments generate sharp but uneven adjustments in surface and atmospheric conditions. While the atmosphere recovers rapidly from rainfall-induced cooling, the soil retains a substantial portion of the cooling anomaly, creating a land-surface memory. Using multi-year, multi-layer observations from five stations in Ningxia, China, and ERA5 reanalysis, this study investigates how this soil thermal memory timescale (τrec) modulates the timing of recycled moisture return. Analysis of 112 events reveals a consistent "cold-humid pulse" with rapid atmospheric recovery but slow soil recovery, whose persistence we quantify as τrec (20–80 hours) using an exponential-decay framework. ERA5 diagnostics show the recycled moisture signal peaks 20–40 hours after rainfall, defining a recycling lag (τRR). Event-wise analysis of ten long-duration events reveals a systematic positive correlation (R ≈ 0.57) between τrec and τRR. Longer soil memory consistently predicts a more delayed recycling peak. We show this relationship is mediated by enhanced moisture-heat feedback (H), where persistent cold soils slow boundary-layer recovery and postpone the reactivation of evaporation. These results identify soil thermal memory as an active regulator; the timing of recycled moisture is not solely an atmospheric process but is partially land-controlled. This work establishes a novel "coupling-memory-recycling" pathway, providing a new mechanism for understanding and modeling dryland precipitation dynamics.