The objectives of this research were to compare precipitation extremes obtained from Tropical Rainfall-Measuring Mission (TRMM) satellite and those of rain gauges over a semiarid area in Iran. Extreme precipitation indices (EPIs) (i.e., the number of days with a precipitation value over 10 mm, the maximum duration of wet and dry days, the number of days with precipitation over the 95th percentile, total precipitation higher than the 95th percentile, and maximum daily precipitation) were calculated across Fars province, Iran, 2000-2014 on seasonal time scales. The gauges data were interpolated at a spatial resolution of 0.25 × 0.25 to match the 3B42 data using Inverse Distance Weighting (IDW). Then, EPIs from the two datasets were compared with each other. The findings showed that mean values computed from gauges and satellite data did not present any significant differences among all of the extreme indices. Furthermore, their variances presented a good level of congruence. Finally, the majority of indices presented a satisfactory correlation between the two dataset. To evaluate the prediction of extreme events in different temporal and tolerated distances, a fuzzy method was used. The results showed that the percentage of grid cells with useful predictions tripled with spatial tolerance extending by just one pixel. To evaluate methods of eliminating the uncertainty of probable missing rainfall data and the seasonal changes in rainfall averages, probabilistic methods based on Weibull distribution and truncated geometric distribution (TGD) were employed to eliminate uncertainties in estimation of extreme precipitation amounts and extreme wet periods (WPs). The results showed that as to extreme precipitation amounts, a satisfactory method could not be drawn for arid southern regions of Fars, Iran. Similarly, as to extreme WPs, the consistency between gauges and satellite data could not be improved significantly.

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Although a number of studies have been conducted on extreme precipitation trends in different parts of the world including Iran, a great number of such studies have reported only the total amount of daily precipitation greater than a certain percentage (e.g., 95%) of the long term data (R95p), ignoring other useful indices. To address this research gap, we used other modified indices, namely R95tot (fractional contribution of very wet days to annual total amounts), R95tt (fractional contribution of very wet days to the total annual obtained from fitted gamma probability distribution), and RS95 (same as R95tt except that it uses Weibull distribution and very wet days defined by 95 percentage of an individual year) by which the spatial and temporal changes of very wet days across Iran was assessed, 1985–2013. In addition, to evaluate the effect of the selected distribution on the results, a new index‐(RS95gm)—was introduced and reported. This index is similar to RS95, except that it uses gamma distribution instead of Weibull. According to trend analysis of R95p, R95tot, and R95tt, reduced frequency of extreme precipitation events was detected in some northwest, west and northeast parts of Iran. On the contrary, RS95 (RS95gm) results showed a higher frequency of extreme events across Iran. It was also demonstrated that while R95p, R95tot, and R95tt were unequivocally affected by changes in the mean wet‐day/ annual total precipitation, RS95 (RS95gm) was more influenced by changes in the distributional shape, showing more stable trends. Although RS95 and RS95gm were highly correlated with only 19% difference on average, their trend analysis results were not completely consistent (70% agreement). Thus, it may be concluded that any changes in statistical distribution in the calculation of the RS95 would have a considerable effect on whether the obtained trend is significant or not.

South American leaf blight (SALB) of Para rubber trees (Hevea brasiliensis Muell. Arg.) is a serious fungal disease that hinders rubber production in the Americas and raises concerns over the future of rubber cultivation in Asia and Africa. The existing evidence of the influence of weather conditions on SALB outbreaks in Brazil has motivated a number of assessment studies seeking to produce risk maps that illustrate this relationship. Subjects with dynamic and cyclical spatiotemporal features need to embody sufficiently fine spatial resolution and temporal granulation for both input data and outputs in order to be able to reveal the desired patterns. Here, we apply emerging hot spot analysis to three decades of gridded daily precipitation and surface relative humidity data to depict their temporal and geographical patterns in relation to the occurrence of weather conditions that may lead to the emergence of SALB. Inferential improvements through improved handling of the uncertainties and fine-scaled temporal breakdown of the analysis have been achieved in this study. We have overlaid maps of the potential distribution of rubber plantations with the resulting dynamic and static maps of the SALB hot spot analysis to highlight regions of distinctly high and low climatic susceptibility for the emergence of SALB. Our findings highlight the extent of low-risk areas that exist within the rubber growing areas outside of the 10° equatorial belt.

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