The Haihe Plain is the largest component of the agriculturally vital North China Plain, and it is characterized by serious water shortage and frequent droughts, which lead to crop reduction and have adverse effects on agriculture and ecology. We used daily precipitation data from 1955–2017; the region’s spatiotemporal characteristics of drought were analyzed by using the standardized precipitation index (SPI), drought probability, and Mann–Kendall test for seasonal scale including two main crops growth seasons for the region’s main crops. Furthermore, a cloud algorithm model was established to analyze the dispersion and instability of the SPI. The annual drought frequency is 28.57%; the SPI for spring has an increasing tendency, while summer shows a significant decreasing trend (p < 0.05); the Haihe Plain has had a tendency towards drought over the last 63 years. The SPI in northwest is the smallest and increases gradually toward the south; the severity of drought in dry years increased from southeast to northwest. The cloud model shows that the SPI randomness of each site decreased significantly and tended to be stable and uniform. The deterministic and stable SPI of each station is stronger in dry years, and the randomness and instability are stronger in wet years. The inter-annual differences of the characteristic values of the SPI cloud model are bigger than the differences among sites, and the inter-annual randomness and inhomogeneity of the SPI are higher.

The ridge–furrow plastic mulching technique has been widely applied due to its benefits of increasing temperature, conserving moisture, reducing evaporation, and boosting yields. Hydrus-2D is a computer model designed to simulate the two-dimensional movement of water in soil characterized by a low cost and high flexibility compared to field experiments. This study, based on field experiment data from Jianping County, Liaoning Province, China, during 2017–2018, developed Hydrus-2D models for two distinct field management practices: non-mulched flat cultivation (NM-FC) and mulched ridge tillage (M-RT). Furthermore, it simulated the dynamic changes in farmland water variations during the summer maize growth period (2021–2100) under climate change scenarios, specifically medium and high emission pathways (SSP2-4.5 and SSP5-8.5), based on the FGOALS-g3 model, which exhibits the highest similarity to the climate pattern of Jianping County in the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and the Shared Socioeconomic Pathways (SSPs). The results showed that in the future FGOALS-g3 model, net radiation exhibited a significant upward trend under the SSP2-4.5 scenario (Z = 2.38), while the average air temperature showed a highly significant increase under both SSP2-4.5 and SSP5-8.5 scenarios, with Z-values of 6.48 and 8.90, respectively. The Hydrus-2D model demonstrated high simulation accuracy in both NM-FC and M-RT treatments (R² ranging from 0.86 to 0.96, with RMSE not exceeding 0.011), accurately simulating the dynamic changes in soil water content (SWC) under future climate change. Compared to NM-FC, M-RT reduced evaporation, increased transpiration, and effectively decreased the leakage caused by increased future precipitation, resulting in a 0.04 and 0.01 cm³/cm³ increase in surface and deep soil SWC, respectively, during the summer maize growing season, significantly improving water use efficiency. Moreover, M-RT treatment reduced the impact coefficients of climate change on various water balance parameters, stabilizing changes in these parameters and SWC under future climate conditions. This study demonstrates the significant advantages of M-RT in coping with climate change, providing key scientific evidence for future agricultural water resource management. These findings offer valuable insights for policymakers and farmers, particularly in developing adaptive land management and irrigation strategies, helping to improve water use efficiency and promote sustainable agricultural practices.