Fire occurrence probability mapping provides a detailed understanding of the spatial distribution of the fire occurrence probability and it is useful in fire management. The binary logistic regression (BLR) can combine continuous and categorical variables together in the analysis. Here we use BLR analysis to map the fire occurrence probability of Northeast China which has the largest forest area in China. Ten predictor variables including altitude (Alt), slope (Sl), aspect (As), distance to the nearest village (Dv), distance to the nearest path (Dp), distance to the nearest water bodies (Dw), land cover (LC), Fuel Moisture Content (FMC), land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) are employed and multi-temporal random sampling methodology is used to create the training subset, and then the training subset is utilized to build the fire occurrence probability spatial model. Here, a backwards stepwise procedure based on the likelihood ratio estimation is used in the model development. Assessed by the area under a relative operating characteristic (ROC) curve (AUC-area under curve) procedure, the model's fitness accuracy is 84.2%. The interpretations of the estimated coefficients show that NDVI best explain fire occurrence in the region. Evaluated by the inner testing and independent validation, better reliability and discrimination capacity of the developed spatial model can be concluded from 17 fires among the total 18 fires. Good performance suggests that the developed model is valuable to fire managers or can be directly applied to fire management in Northeast China.Index Terms-Fire danger, fire occurrence probability, logistic regression, Northeast China.

Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the ''standard deviation'' operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination(R 2 ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.INDEX TERMS Variation predictor, spatial downscaling, disaggregation, unmixing, scaling, geographically weighted regression (GWR), tropical rainfall measuring mission (TRMM), precipitation, north-south transitional zone of China.The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .