Land and forest fire have been identified as one of the main problems contributing to forest biodiversity and Global Warming and well known as the phenomenon affected by El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The total burned area becomes higher when either El Niño or positive IOD occur. This research aims to analyze and quantify the direct correlation of the Niño 3.4 and difference between west and east pole of IOD sea surface temperature anomaly (SSTA) to the burned area in Indonesia and the impact of ENSO and IOD of each category on the burned area. The correlation between spatial location with Niño 3.4 and difference IOD SST's will be analyzed using a heterogeneous correlation map. Meanwhile, the quantitative impact will be calculated based on the singular value decomposition analysis result to each year categories. The most significant impact of El Niño has occurred on Merauke following Kalimantan shows the strongest correlation between burned area and Niño 3.4 SST. However, the significant increase of burned area only occurred during very strong El Niño. Both areas can have double amount of burned area during peak fire in very strong El Niño. Moderate El Niño have the most diverse impact with the stronger one occurs on Kalimantan and Merauke. Weak El Niño can have a significant impact if occurred simultaneously with positive IOD. Even more, it can surpass the effect of a single Moderate El Niño. Meanwhile, the strongest IOD impact happened in the southern part of Sumatra.

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El Nino is a global climate phenomenon caused by the warming of sea surface temperatures in the eastern Pacific Ocean. El Nino has a powerful effect on the intensity of rainfall in several areas in Indonesia. El Nino impacts can be minimized by predicting the El Nino index from the sea surface temperature in the Nino 3.4 area. Therefore, many researchers have tried to predict sea surface temperature, and many prediction data are available, one of which is ECMWF. But, in reality, the ECMWF data still contains systematic errors or bias towards the observations. Consequently, El Nino predictions using ECMWF data are less accurate. For that reason, this study aims to correct the ECMWF data in the Nino 3.4 area using statistical bias correction with a quantile mapping approach. This method uses ECMWF data from 1983-2012 as training data and 2013-2018 as testing data. For this case, the results showed that 60% of El Nino's predictions on the testing data had improved the mean value. Also, all of El Nino's predictions on the testing data have improved the standard deviation value. Moreover, data testing's expected error can be corrected for all months in the 1st to 4th lead times. But, in the 5th to 7th lead times, only November-June can be corrected.

Rainfall patterns in Kalimantan are generally divided into 2 types, namely monsoonal and equatorial. The pattern can be determined by analyzing the 6-month frequency of rainfall signal. This analysis has been carried out on general data in Indonesia, but no one has yet examined it in detail in Kalimantan. Therefore, this study will analyze the 6-month frequency signal and rainfall patterns spatially and temporally in Kalimantan using TRMM 3B42RT as the main data. The Fast Fourier Transform (FFT) method is applied to analyze the 6-month frequency of rainfall signal, while the Empirical Orthogonal Function (EOF) method is applied to reduce data and obtain the main pattern of rainfall in Kalimantan. The results of FFT analysis in 15 cities of Kalimantan show that the rainfall pattern in Samarinda, Sendawar, Tarakan, Tanjungselor, Malinau, Pangkalanbun, Pontianak, Ketapang, and Sintang are an equatorial type, while a monsoonal type appear in Balikpapan, Palangkaraya, Purukcahu, Banjarmasin, Kotabaru and Barabai. Moreover, based on the results of FFT and EOF analysis, most areas in West, East and North Kalimantan have an equatorial rainfall pattern. Meanwhile, most areas in Central and South Kalimantan have a monsoonal rainfall pattern.

El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. Based on variance explained and correlation strength, the hotspot in Indonesia is more sensitive to ENSO and IOD derived from ERA5 than OISST. Consequently, the ERA5 data more useful to statistical analysis that requiring a substantial correlation.