陆地生态系统固碳是实现碳中和的途径之一，植树造林能够增强陆地生态系统碳汇能力，是应对气候变化的一项重要措施。根据《全国森林经营规划（2016－2050）》，未来中国还将进行大规模的植树造林活动（约74万km²），这必然会对中国区域气候造成巨大影响。因此，亟待对这种影响进行科学评估。本文利用ERA5再分析资料和订正后的MPI-ESM1-2-HR模式资料驱动WRF（Weather Research and Forecasting，简称WRF）模式，针对未来（2041至2060年）气候变化情景下中国区域造林与不造林下垫面，开展了25km分辨率的区域气候变化模拟试验，揭示了造林对区域气候的影响，并诊断了其物理机制。具体开展的工作及结果如下：

（1）评估了CMIP6中14个地球系统模式逐6小时间隔数据的精度。综合多变量（气温、位势高度、比湿、纬向风和经向风），多气压层（850hPa、500hPa和250hPa）和多指标（气候态、年际变率、年循环和日循环）的模拟表现，发现MPI-ESM1-2-HR模式最适用于中国地区动力降尺度。进而，采用气候平均态和年际变率振幅联合订正法对MPI-ESM1-2-HR模式进行偏差订正，并利用订正后的历史数据驱动WRF模式进行了模拟试验。对比分析发现，订正后的MPI-ESM1-2-HR模式与ERA5驱动WRF模式对中国地区气温和降水量的模拟结果基本一致，二者模拟气温和降水的偏差分别为0.169℃和-0.017 mm/天。这表明，MPI-ESM1-2-HR模式数据可以用于驱动WRF模式开展区域气候变化模拟。

（2）基于国家造林规划和气候变化情景，辨识了未来中国造林区域范围。基于WRF模式输出的未来高分辨率气温和降水数据，结合Holdridge生命地带模型和《全国森林经营规划（2016－2050）》，发现未来中国潜在造林区主要分布在胡焕庸线附近。超过80%网格的土地利用转移类型为草地转落叶阔叶林和萨瓦纳/木质萨瓦纳转常绿阔叶林。

（3）通过对比SSP245情景下2041－2060年中国造林与未造林的数值试验结果，发现未来造林将导致中国造林区平均地表反照率减少0.024，叶面积指数增加1.588 m²/m²，气温降低0.099℃。在草地转落叶阔叶林的华北地区，净辐射，感热和潜热增加，最终将导致造林区气温升高0.184℃。而在木质萨瓦纳转常绿阔叶林的南方地区，净辐射减少，感热减少而潜热增加，最终将导致造林区气温将降低约0.5 ~ 0.8℃。未来造林将导致中国区域年总降水量增加3.844mm。在暖季的东北地区，造林能够导致水汽输送异常增加，形成了水汽辐合中心，增加了当地云量和大气可降水量。同时，造林将导致垂直上升速度、绝对涡度和对流有效位能增加，对流运动旺盛，最终导致东北地区降水增加19.183mm。而在西南地区，尽管当地对流运动异常增加，但由于受异常反气旋性环流的影响，大气水汽含量亏损，最终导致降水量减少3.77mm。

（4）通过对比造林和全球变化对中国区域气候的影响幅度，发现如果未来中国进行大规模植树造林，SSP245情景下2041－2060年中国气温相较于历史时期将升高0.792℃，降水将增加41.788mm。其中，全球变化对中国气温的升高贡献为95.342%，而造林贡献为4.658%。就区域而言，尤其以华北地区造林对变暖的贡献率最大，为21.650%。同理，造林对于中国年降水量的增加贡献达到14.429%，东北地区贡献率最大，为44.749%。

Terrestrial carbon sequestration is one of the reasonable approaches to achieve carbon neutrality. Afforestation is considered as a solution to enhance ecosystem carbon sink and mitigate climate warming. The National Forest Management Planning (2016－2050) indicates that large-scale afforestation programs (~74 million km²) will also be implemented in China in the future, which will have a huge impact on the regional climate in China. Therefore, a scientific assessment of this impact is urgently needed. In this study, using the ERA5 reanalysis data and the bias-corrected MPI-ESM1-2-HR model nested with the WRF (Weather Research and Forecasting) model, we conducted numerical experiments with a spatial resolution of 25 km. By comparing the differences between afforestation and non-afforestation in China, we investigated the effects of afforestation on regional climate change and explored the possible physical mechanism from 2041 to 2060. The specific work and main results are as follows:

We evaluated the overall performance of 6-hour interval CMIP6 models by combining the simulated performance of multiple variables (temperature, geopotential height, specific humidity, latitudinal and meridional winds), multiple pressure layers (850hPa, 500hPa, and 250hPa) and multiple indexes (climatology, interannual variability, annual cycle, and diurnal cycle). The results showed that the MPI-ESM1-2-HR model was the optimal model for dynamic downscaling over China. Then, the ERA5 and bias-corrected MPI-ESM1-2-HR model were nested by the WRF model to simulate the climate change in China. The consistent simulation for temperature and precipitation of the MPI-ESM1-2-HR model and ERA5 nested WRF model were found. The bias of simulated temperature and precipitation was 0.169℃ and -0.017 mm/d, respectively. Thus, the bias-corrected MPI-ESM1-2-HR model could replace the ERA5 reanalysis data as the lateral boundary conditions of WRF to obtain the same accuracy with high-resolution simulations.

Based on the national afforestation programs and climate change scenarios, the extent of future afforestation in China was identified. Based on the high-resolution future temperature and precipitation data output from the WRF simulation, combined with the Holdridge Life Zone model and the National Forest Management Plan (2016－2050), the scope of future afforestation in China was identified. We found that the future potential afforestation regions in China were mainly distributed near the Hu line. The main transfer types of land use were grassland to the deciduous broadleaf forest and savanna/woody savanna to evergreen broadleaf forest.

By comparing the numerical experiments of afforestation and non-afforestation in China under the SSP245 scenario from 2041 to 2060, we found that future afforestation decreased the surface albedo by 0.024 and increased the leaf area index by 1.588 m²/m². In northern China, the transfer type was grassland to deciduous broadleaf forest. The annual temperature affected by afforestation was increased by 0.184°C in the afforested area due to the increased net radiation, sensible and latent heat fluxes. In southern China, the transfer type was woody savanna to the evergreen broadleaf forest. The annual temperature was decreased by 0.5 ~ 0.8°C in the afforested area due to the decreased net radiation and sensible heat flux, and increased latent heat flux. In northeastern China, future afforestation led to an increase in water vapor fluxes anomalies and the formation of water vapor convergence centers, which increased cloud cover and atmospheric precipitable water. At the same time, the vertical velocity, absolute vorticity, and convective available potential energy increased due to afforestation. Thus, the precipitation was increased by 19.183 mm due to sufficient moisture and intensified convective process. In southwest China, future afforestation increased the convective process, but the water vapor decreased due to anticyclonic circulation anomalies. The precipitation induced by afforestation was ultimately decreased by 3.77 mm.

By comparing the magnitude of the impact of afforestation and global change on regional climate, we found that if large-scale afforestation was implemented in China in the future, the temperature would increase by 0.792°C and precipitation will increase by 41.788 mm in 2041－2060 under the SSP245 scenario compared to the historical period. The contribution to the temperature increase of global change and afforestation was 95.342% and 4.658%, respectively. Regionally, the largest contribution was in northern China with 21.650%. Similarly, afforestation contributed 14.429% to the increase in annual precipitation in China. The largest contribution of 44.749% was in northeast China.