Hydrological models serve as useful tools for flood forecasting and water resource management (Bárdossy & Singh, 2008). Rainfall data are the primary input for hydrological models. Usually, rain gauge observations are considered the most reliable rainfall source (so-called "ground truth"). However, due to the insufficient rain gauge density, rainfall estimations from weather radars, satellites, reanalysis, and Numerical Weather Prediction (NWP) model products have also been widely applied for hydrological applications.A plethora of studies employed hydrological modeling to evaluate the reliability of various state-of-the-art rainfall data sets in different regions. Many of them suggested that despite the varied accuracy of rainfall data compared with observations, some rainfall data sets could reproduce satisfactory streamflow simulations after model recalibration, and could even perform as well as using the observed rainfall (

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High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

The quality of precipitation (P) and potential evapotranspiration (PET) data greatly affects the hydrological modeling performance. Considerable attention has been paid to identifying the influence of biased P or PET inputs independently. However, few studies have explored the joint interaction of biases in P and PET inputs on hydrological simulations. Here, we investigate the mutual compensation of P and PET biases on the performance of two widely used conceptual hydrological models, the Xinanjiang model and the Probability Distributed Model. P and PET from HYREX (HYdrological Radar EXperiment) and CAMELS‐GB (Catchment Attributes and Meteorology for Large‐sample Studies in Great Britain) data sets are collected over five catchments with varying characteristics in Great Britain. Different biases are added to these original time series to generate 6560 biased input scenarios. The results suggest that there is a certain compensational relationship between the biases in P and PET inputs to reproduce desirable streamflow simulations. A new hydrological proxy named Compensational Interaction Angle (CIA) is identified and found to be stationary with various modeling periods, as well as stable with different hydrological models despite model equifinality. Further, the CIA highly relates to the long‐term climate aridity ratio. The catchments with greater aridity have larger CIAs. This study offers a fresh perspective to analyze the input errors in hydrological modeling. The results can help to better understand P and PET interactions in hydrological modeling, and guide the selection/evaluation/bias‐correction of P and PET data sets for hydrological applications.