1. Introduction and Objective

Accurate and high-resolution precipitation data are fundamental for hydrological modeling, water resource management, and climate change impact assessments. In countries like Iran, which are characterized by complex topography and a disproportionate distribution of ground-based meteorological stations, reanalysis datasets serve as a vital alternative to fill spatial and temporal gaps. The ERA5-Land dataset, providing a 0.1° (~10 km) spatial resolution, is often considered a premier source for such data. This study presents a rigorous evaluation of ERA5-Land daily precipitation estimates over Iran for a 30-year period (1991–2020). The primary objective is to assess the model’s capacity to reproduce overall spatial patterns, discriminate between precipitation intensities, and accurately detect extreme weather events across the country’s five distinct climatic clusters.

2. Methodology

The evaluation employed a multi-dimensional framework comparing ERA5-Land daily data against a gridded observational dataset derived from station records. The methodology was divided into three main analytical categories:

• Quantitative Indices: To evaluate error and agreement, the Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Relative Bias (RBIAS), and Spearman Rank Correlation were utilized.

• Spatial Indices: The Spatial Efficiency metric (SPAEF) was applied to assess spatial pattern accuracy at daily, monthly, and annual scales. Furthermore, Empirical Orthogonal Function (EOF) analysis was conducted to compare the dominant modes of spatial variability (EOF1) between the model and observations.

• Probabilistic Indices: To evaluate the model’s skill in rain detection and extreme event identification, the Probability of Detection (POD), False Alarm Ratio (FAR), and Heidke Skill Score (HSS) were calculated.

To account for Iran’s significant climatic heterogeneity, the study area was partitioned into five climatic clusters ranging from hyper-arid central deserts to the humid Caspian belt.

3. Results and Discussion

3.1. Overall Pattern and Intensity Discrimination

The results indicate that ERA5-Land generally captures the macroscopic structure of precipitation in Iran, yielding an overall overlap index of 0.85. However, a significant performance gap emerges when analyzing specific intensity classes. The model struggles with both ends of the precipitation spectrum. For extreme precipitation events, the overlap index drops sharply to 0.26, revealing a distinct “smoothing effect”—where the model underestimates the magnitude of intense storms while overestimating the frequency of very light rainfall. This suggests that while the model is reliable for general trends, it lacks the precision required for raw intensity-class analysis without prior correction.

3.2. Spatiotemporal Accuracy and Variability

The spatial accuracy of the model, measured by SPAEF, exhibits a strong dependence on the temporal scale. At the daily scale, the mean SPAEF is relatively low (0.291), indicating difficulty in simulating localized convective and short-duration events. However, performance improves significantly as the data is aggregated: monthly SPAEF rises to 0.623, and annual SPAEF reaches 0.723. This confirms that ERA5-Land is highly reliable for medium- to long-term hydroclimatic monitoring. Furthermore, the EOF1 analysis shows a remarkable spatial correlation of approximately 0.96 between ERA5-Land and observations, confirming that the model accurately reproduces the primary modes of precipitation variability across the Iranian plateau.

3.3. Regional Performance and Climatic Clusters

The performance of ERA5-Land is highly sensitive to regional geography and local climate:

• Western and Southwestern Iran (Zagros Mountains): The model performs optimally here, as precipitation is largely driven by large-scale Mediterranean synoptic systems which reanalysis models simulate well.

• Cluster 3 (Southern/Southeastern Coasts): This region showed the highest overall skill (KGE ≈ 0.56) and the lowest bias (RBIAS ≈ 10%).

• Cluster 4 (Northwest Highlands): This region presented the poorest performance, characterized by a KGE near zero and a massive overestimation bias (RBIAS ≈ 90%). The complex topography of the northwest poses a significant challenge to the model’s land-surface schemes.

• Cluster 5 (Caspian Belt): While accuracy is acceptable (KGE ≈ 0.46), this cluster exhibits the highest absolute errors (RMSE ≈ 8 mm) due to the high volume of annual precipitation and the prevalence of localized maritime effects.

• Cluster 1 (Dry Central/East): The model shows moderate accuracy but tends to overestimate total precipitation by approximately 30%.

3.4. Event Detection and Extremes

In terms of daily rain detection, ERA5-Land is most effective in the west and southwest, where synoptic-scale systems dominate. In the east and southeast, where rainfall is more sporadic and localized, the model’s skill declines. Regarding extreme events (above the 66th percentile), the POD decreases across all clusters. While Cluster 3 shows the best skill for extremes (HSS > 0.6), Cluster 4 is plagued by high FAR, and Cluster 1 shows the lowest capability in identifying above-normal events.

4. Conclusion

ERA5-Land proves to be a valuable asset for monitoring precipitation patterns in Iran, particularly for climate studies focusing on monthly or annual scales and for regions dominated by synoptic-scale weather systems. Its ability to capture the dominant spatial variability (EOF1) makes it suitable for long-term climatic trend analysis. However, its limitations are evident at the daily scale and in the detection of extreme events. The systematic smoothing of intensities and the significant overestimation in mountainous regions like the northwest indicate that ERA5-Land data should be used with caution in hydrological flood modeling or local-scale agricultural studies. The study concludes that while ERA5-Land is a robust tool for general monitoring, post-processing and bias correction are essential requirements for applications requiring high-precision daily or extreme precipitation data.