With global warming, agrometeorological disasters are also rising, posing a severe threat to China’s food security. Risk assessment serves as a bridge from disaster crisis management to risk management. Gansu Province is geographically crucial, so we performed a refined assessment of grain production risk for this province using multiple features of disaster loss data recorded at the county level. Analyses were performed for each district and county with a probability approach and an index system. We found that grain trend yields in each district and most counties in Gansu Province are increasing. Wuwei and Linxia districts had higher yearly growth rates, of more than 120 kg/(ha·year). However, there are considerable differences in risk levels among counties, even within the same district. Huating and Jinchang counties are high risk locations, while Cheng, Diebu, Jinta, and Xiahe counties are low risk zones. In 39.2% of counties, the fluctuation tendency rate of relative meteorological yield was positive. The average yield reduction rates of grain in the 1980s, 1990s, 2000s, and 2010s were 5.5%, 6.6%, 8.1%, and 4.2%, respectively, and the average fluctuation coefficients were 5.0%, 5.5%, 7.1%, and 3.8%, respectively. After 2010, most regions’ average yield reduction rates fell dramatically, and grain output progressively stabilized. Counties prone to heavy disasters are primarily spread along the Hexi Corridor, with the probability exceeding 8%. However, 27.9% of counties were spared from severe calamities, which were mainly distributed in southwestern Gansu Province. Crop disaster conditions significantly positively correlated with grain risk. Drought is the primary cause of grain yield decline in Gansu Province. The findings can provide essential policy advice for the government in disaster prevention.

The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention.

Food security is crucial for human survival and national economic development, but frequent meteorological disasters have caused great harm to agricultural production. Therefore, it is very important and meaningful to study how to quickly and accurately predict the loss rate of disasters. Only based on historical loss sequence, the time series prediction method can effectively predict future loss. Therefore, this paper first briefly describes the main means of time series prediction, namely statistical methods and machine learning algorithms. Secondly, the commonly used machine learning algorithms for disaster loss time series prediction, and its application cases and existing problems, were introduced in detail. To address the issue of small sample sizes for loss predication, data augmentation techniques can be used; To address the issue of data non-stationarity, Empirical Mode Decomposition (EMD) can be used to decompose the original sequence into relatively stationary sub-sequences. In addition, exploratory solutions have been proposed, such as ensemble learning strategies for multiple machine learners, and combining machine learning algorithms with optimization algorithms, strong prediction strategies, or attention mechanisms. Finally, a summary solution for conventional disaster damage prediction was proposed.