Fire plays a critical role in both the formation and degradation of ecosystems; however, there are still significant uncertainties in the estimation of burned areas (BAs). This study systematically evaluated the performance of ten global climate models (GCMs) in simulating global and regional BA during a historical period (1997–2014) using the Global Fire Emissions Database version 4.1s (GFED4s) satellite fire product. Then, six of the best models were combined using Bayesian Model Averaging (BMA) to predict future BA under three Shared Socioeconomic Pathways (SSPs). The results show that the NorESM2-LM model excelled in simulating both global annual and monthly BA among the GCMs. GFDL-ESM4 and UKESM1-0-LL of the GCMs had the highest Pearson’s correlation coefficient (PCC), but they also exhibited the most significant overestimation of monthly BA variations. The BA fraction (BAF) for GCMs was over 90% for small fires (50%). After evaluation and screening, six models (CESM2-WACCM-FV2, NorESM2-LM, CMCC-ESM2, CMCC-CM2-SR5, GFDL-ESM4, and UKESM1-0-LL) were selected for weighting in an optimal ensemble simulation using BMA. Based on the optimal ensemble, future projections indicated a continuous upward trend across all three SSPs from 2015 to 2100, except for a slight decrease in SSP126 between 2071 and 2100. It was found that as the emission scenarios intensify, the area experiencing a significant increase in BA will expand considerably in the future, with a generally high level of reliability in these projections across most regions. This study is crucial for understanding the impact of climate change on wildfires and for informing fire management policies in fire-prone areas in the future.

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models.

Recently, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) mission and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) precipitation datasets have been widely used in remote sensing and atmospheric studies, respectively, because of their high accuracy. A dataset of 268 site-gauge precipitation measurements over the Yellow River Basin in China was used in this study to comprehensively evaluate the performance of three high-resolution precipitation products, each with a spatial resolution of 0.1°, consisting of two satellite-derived datasets, IMERG and multisource weighted-ensemble precipitation (MSWEP), and one ERA5-derived dataset, ERA5-Land. The results revealed that the spatial distribution of IMERG annual precipitation closely resembled that of the observed rainfall and generally exhibited a downward trend from southeast to northwest. Among the three products, IMERG had the best performance at the annual scale, whereas ERA5-Land had the worst performance due to significant overestimation. Specifically, IMERG demonstrated the highest correlation coefficient (CC) above 0.8 and the lowest BIAS and root mean square error (RMSE), with values in most regions of 24.79 mm/a and less than 100 mm/a, respectively, whereas ERA5-Land presented the highest RMSE exceeding 500 mm/a, BIAS of 1265.7 mm/a, and the lowest CC below 0.2 in most regions. At the season scale, IMERG also exhibited the best performance across all four seasons, with a maximum of 17.99 mm/a in summer and a minimum of 0.55 mm/a in winter. Following IMERG, the MSWEP data closely aligned with the observations over the entire area in summer, southern China in spring and winter, and middle China in autumn. In addition, IMERG presented the highest Kling–Gupta efficiency coefficient (KGE) of 0.823 at the annual scale and the highest KGE (>0.77) across all four seasons among the three products compared with ERA5-Land and MSWEP, which had KEG values of −2.718 and −0.403, respectively. Notably, ERA5-Land exhibited a significant positive deviation from the observations at both the annual and seasonal scales, whereas the other products presented relatively smaller biases.

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions.

Accurate precipitation estimation is crucial for water management and flood reconstructions, yet remains challenging due to sparse ground observations and systematic satellite biases. Despite the rise of deep learning in multi-source fusion, controlled comparisons under a unified framework remain scarce, and the roles of window size and precipitation product combinations remain underexplored. This study evaluated five deep learning architectures, namely 1D CNN, 2D CNN, 1D–2D CNN, 3D CNN, and Transformer, for fusing three precipitation products (IMERG, MSWEP, and ERA5-Land) to estimate station-scale daily precipitation in the Yellow River Basin, China. Results showed that deep learning models consistently outperformed individual products, weighting methods, and machine learning methods, with Transformer achieving the best overall performance, followed by 3D CNN; MSWEP led the standalone products. Spatial improvements were more pronounced in densely gauged areas, but Transformer also performed well in the sparse western regions. All products and fusion models tended to overestimate no-rain and light-rain events and underestimate moderate-to-heavy, and extreme events, with Transformer yielding the largest reductions in conditional RMSE and BIAS for heavy and extreme events. Enlarging the spatial or temporal window individually improved performance, and combining both worked even better. Ablation experiments revealed that IMERG contributed little unique information, whereas incorporating more products generally improved Transformer performance. Overall, Transformer demonstrated superior performance in fusing precipitation products and enhancing estimation accuracy.